HDR image encoding and decoding methods and devices

ABSTRACT

An image encoder includes an input for a high dynamic range input image (M_HDR); an image grading unit arranged to allow a human color grader to specify a color mapping from a representation (HDR_REP) of the high dynamic range input image defined according to a predefined accuracy, to a low dynamic range image (Im_LDR) by a human-determined color mapping algorithm. The image encoder is configured to output data specifying the color mapping (Fi(MP_DH)). Further, the image encoder includes an automatic grading unit configured to derive a second low dynamic range image (GT_IDR) by applying an automatic color mapping algorithm to one of the high dynamic range input image (M_HDR) and the low dynamic range image (Im_LDR).

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is the U.S. National Phase application under 35 U.S.C.§371 of International Application No. PCT/IB2014/058848, filed on Feb.7, 2014, which claims the benefit of U.S. Provisional Application No.61/767,314, filed on Feb. 21, 2013 and U.S. Provisional Application No.61/868,111, filed on Aug. 21, 2013. These applications are herebyincorporated by reference herein.

FIELD OF THE INVENTION

The invention relates to apparatuses and methods and resulting productslike data storage products or encoded signals, e.g. as stored inmemories, for improved encoding of at least one image or video with anincreased dynamic luminance range compared to legacy images called lowdynamic range (LDR) images.

BACKGROUND OF THE INVENTION

The newly emerged field of High Dynamic Range (HDR) imaging contrastsitself with legacy systems, which nowadays by comparison we can call lowdynamic range (LDR) imaging (an which comprise such image or videoencoding systems like PAL or MPEG2, AVC, HEVC or another member of theMPEG family, or similar video standards like e.g. VC1, VC2, etc., orJPEG for still pictures etc.)

When talking about HDR, we need to look at the various components of thechain. As this is a very recent area of technology where perhaps noteverybody is on the same line, we want to quickly set a referencemindset with a couple of important definitions, to avoidmisunderstandings. Ultimately there is the rendering dynamic range,which the display medium can generate. Rendering dynamic range isusually defined as RDR=brightest_white_luminance/darkest_black_luminanceof all pixels in an image (intra-picture RDR) or of pixels in successiveimages (inter-picture RDR, e.g. when the display is (nearly) switchedoff, and one only sees the reflection of the surrounding environment onthe front glass). However, it is more meaningful when also coupled to apeak_white value (i.e. the brightest_white_luminance value). LDRrenderers usually lie in or around a range defined by peak_white of 100nit, and a dynamic range of around 100:1. That is what a CRT displaymight have produced, where of course the darkest_black_luminancestrongly depends on the viewing environment illumination, so one may gofor 40:1 to be on the safe side, and also 2:1 can be a practical dynamicrange when one views images on a display under the sun. The viewingenvironment which conditions the human viewer brightness adaptation isrelated to that, e.g. typically 20% of peak_white. Several standards ofEBU, SMPTE etc. specify how one should grade a video signal so that itcan be used in a standard way, e.g. it is optimal if shown in theprescribed viewing environment. By grading we mean producing an imagewith changed pixel colors, which are changed/specified according to somepreference. E.g., a camera can automatically grade a RAW camera picture(which is just dependent on the camera specifics as a linear luminancemeasuring instrument) given a rendering intent into a directly usabledisplay-referred encoding, with which one can steer e.g. such a CRTdisplay under reference conditions so that it will show a neat pictureto the viewer.

Oftentimes grading by a human involves more artistic choices. E.g. thegrader wants to make the color of a plant a nice purplish color, butthis needs to be specified under reference conditions (both of thedisplay technology and the viewing environment, and in theory also otherconditions affecting the state of the viewer like medicament uptake, butof course one typically ignores those largely), because a particulardisplay may make this color more bluish, in which case the desiredartistic effect (of creating a beautiful picture) may be gone. It is nottypical that a camera automatically creates the optimal kind of purple,so that is why the grader does that with image-processing software. Sucha grader can be both a photographer, or a visual artist working on amovie, or even somebody working on a (potentially even life) televisionprogram. Of course various applications will have various degrees ofgrading complexity linked to the desired technical and/or artisticquality for those applications. Typically the above standards prescribethat a grading shall be done on a reference monitor of around 100 nit ina reference environment. The question is then how a color will berendered and perceived in practice. Graphics artists for printed presspublications also generate their work under reference conditions to havesome common ground, and avoid needless sources of error e.g. at theprinter's. However, that doesn't mean of course that each reader of thebook or magazine will read the book under a calibrated D50 lamp, butrather he may perceive more dull colors when reading in his bed underbad illumination. The same happens when a movie or television program,or a consumer photo, is shown on a non-reference display from among themany different displays that are available nowadays. E.g., the image(grading) may be shown on a 500 nit peak_white display. What happensthen is that one brightens all pixel colors by at least linearstretching, which occurs by driving the display with the grading, i.e.mapping maximum white (e.g. value R=G=B=255) to the peak_white of thedisplay (of course there may be further brightness deformation for thevarious image pixel colors if the display has a special nativeelectro-optical transfer function EOTF, but usually that is handledinternally to make the display behave like a brighter version of areference CRT, i.e. with a display gamma of around 2.5).

Now such standardized (produced in a reference environment inter alia ona 100 nit reference display) LDR gradings can be used (i.e. lookreasonably good, i.e. still reasonably similar to how they would lookunder reference conditions) on a range of display and/or environmentconditions around the reference display system (i.e. 100 nit peak_whiteetc.). This is because most humans are not so supercritical about theexact (absolute) look of colors since the brain works relatively (e.g.depending on the criteria for allowability, face colors which are one ofthe more critical colors may vary from paleish almost white, to quiteorangeish, etc., before the less critical larger part of the populationstarts to object), but also because for many objects nobody knows whatthe original colors in the scene were. Partially this is also so becauseLDR scenes are made with an “around the average” object color strategy(which is realized inter alia with well controlled studio lighting,maybe not always so anymore with the various on-the-fly content we havenow), which means all colors are vivid, one may even brighten the imagesomewhat to above the 18% level, with some shadows but not too deep orimportant etc., and that reproduces both physically and psychologicallyrather well on various systems. It is e.g. how naïve painters workbefore they discover such complex issues like clair obscure etc. Sodepending on the quality criterion defining acceptable similarity, theLDR_100 nit grading may be used e.g. on displays from 30 nit up to 600nits, and viewing environments from 3× less bright to 5× more bright.The latitude for using a grade can be increased by modifying it with aso-called display transform. The brightness of a display and surrounding(related to Stevens effect and Bartleson_Brenneman effect) can becorrected to a reasonable degree far more easily than issues related todisplay gamut constraints, and one typically can process the picturewith gamma functions or similar. E.g. when moving a display from a dimsurround to a dark surround (or in fact switching off the cozy livingroom viewing lights), one changes from an extra gamma of 1.25 to 1.5i.e. one uses the residual gamma to increase the contrast of therendered images, because human vision is more sensitive in the darkhence perceives the blacks of the rendered image as more grayish, whichamounts to a reduction in perceived contrast which has to becompensated. A similar LDR technology is printing. There of course onedoes not have a priori control over the surround illuminance determiningthe peak_white of the print, but at least, just as with all reflectiveobjects, the white-black RDR is about 100:1 (depending on paper quality,e.g. glossy vs. matte, inks, etc.).

A complication arises when one needs to reproduce an image of a scenewith huge dynamic range, and typically also scene conditions very unlikethe rendering conditions. E.g. in a night scene they eye may be lookingat a scene dynamic range SDR between car lights of 100,000 nit (or e.g.even more for a high pressure sodium or mercury lamp in the scene)versus dark regions in shadows of fractions of a nit. Even in daylight,where it may be more difficult to create dark shadows from theall-pervasive illumination, indoors it may typically be 100× darker thanoutdoors, and also dark clouds, forrest cover, etc. may influence neededluminances (whether captured or to be rendered), if not in intra-scene,then at least in inter-picture i.e. temporally successive reproduction.Quotes for the “native dynamic range” of human vision vary between10.000:1 and 100.000:1 and even 1.000.000:1, because this depends ofcourse on the conditions (e.g. whether one needs to see a darker smallregion in the brights, or vice versa whether one can see some brightsmall object in the dark, be it perhaps partially rhodopsin-bleaching;whether one considers an amount of glare discomforting, etc.; and thenthere is of course also a psychological factor [taking into account suchthings as importance of certain objects, their perfect or sufficientvisibility, emotional impact on the viewer, etc.], leading to thequestion how much of that should be rendered on a display [e.g. a viewermay quickly discard an area as “just black” without caring which blackexactly], given that the viewer is in a totally different situationanyway [not really on holiday, or not really interrogated by a policeofficer shining a light in his face], but one wants a certain amount ofrealism which may further be a trade-off with other factors like e.g.power consumption, so one could pragmatically in fact define severalhuman vision dynamic ranges, e.g. one for a certain type of real sceneviewing, and one for television viewing). E.g. if one is adapted to thedark night sky, but sees the moon in the corner of the eye, that hasless influence on how the rods in other places of the retina can see thefaint stars, i.e. “simultaneous” viewable dynamic range will be high.Conversely when the eye is bathed in strong daylight (over a large areaof its field of view) it is more difficult to discriminate the darkercolors in a darker interior seen and illuminated through a small hole orwindow, especially if a bright source is adjacent to that dark area.Optical systems will then show several glare phenomena. Actually thebrain usually may not even care about that dark interior, and just callall those colors psychological blacks. As another example of how theleakage of light influences and determines scene dynamic range from theperspective of a human viewer, consider a badly illuminated dark bush inthe night behind a light pole. The lamp on the light pole creates alight scattering profile on the scratches of the glasses of the viewer(or if he doesn't wear glasses the irregularities in his eye lens, e.g.submicron particles, water between cells, . . . ), in particular as ahalo around the lamp which reduces the discrimination possibility of thedark colors of the bush behind it. But when the viewer walks a couple ofseconds the lamp moves behind him outside the capturing zone of the eyelens, and the eye can quickly adjust to find the predator lurking in thedark.

So however one defines the useful dynamic range of a scene for encodingand rendering for human consumption (one may even consider not to onlyencode the intra-picture luminances with a global lightness scalingfactor, but the actually occurring luminances from a sunny tropicenvironment to the darkest overcast night), it is clear that far morethan 100:1 is needed for faithful or at least plausible rendering ofthese environments. E.g. we desire our brightest object on a display fordim surround to be around 10000 nit, and our darkest 0.01 nit (or atleast 0.1 nit), at least if we could e.g. dim the lights in case we havefully or mostly dark scenes in the movie or image(s).

This is where HDR comes in. And also, when one captures such a scene itneeds very complex mathematical mapping to approximate it (or even beable to render it) on an LDR display (this in fact oftentimes being notreally possible). E.g. some HDR-to-LDR mapping algorithms use localadaptation to kind of equalize out the illumination field leaving in theLDR rendering mostly an impression of the object reflections i.e.colors. In view of the leakage (multiple reflection, scattering, etc.)of light from brighter to darker parts of a scene it is not easy tocreate extremely high dynamic range scenes, but an illuminationdifference of 100:1 can easily be achieved in many practical situations.E.g. an indoors scene may have (of course dependent on depth of theroom, size and position of the windows, reflectivity of the walls, etc.)a fraction or multiple of about 1/100^(th) of the outdoors (il)luminance(which is also how the daylight factor of building lighting is defined).Higher SDRs can be obtained when watching a sunny outdoors from within acave through a small crack, etc. Also on the display rendering side, aHDR range starts where one starts seeing new appearance concepts. E.g.,on bright displays like a 5000 nit SIM2 display, one can given the rightinput pictures (rightly graded) realistically render impression of realswitched-on lamps, or real sunny landscapes. In distinction with theabove LDR range, we may typically say that HDR starts for normaltelevision living room viewing conditions from around a 1000 nitpeak_white and above, but more precisely this also depends on the exactviewing conditions (e.g. cinema rendering, although with a peak_white of50 nit, already shows quite some HDR appearances). To be even moreprecisely in view of eye and brain adaptation the HDR-ish look innumerical detail would also depend somewhat not just on the physicalluminances but also the image content, i.e. the chosen grading. But inany case there is a clear discrimation between LDR rendering whichmainly shows a dull, lightless version of the scene, as if it was nearlyilluminated homogeneously and just showing the object reflectances, andHDR, in which a full lighting field appearance is superimposed. If youcan then render reasonable blacks, e.g. 1 nit or below, you can indeedget above an LDR contrast range of k×100:1, where k is typically 2-3(which under a particular paradigm of near-similar, i.e. with onlyperhaps a small contrast stretch, relative rendering of the displayedluminances compared to the scene luminances would correspond to asimilar DR in the scene). On the high end of brightnesses it is partly amatter of taste where the brightness should end, in particular wherefurther brightness only becomes annoying. We found that to grade severalkinds of HDR scene 5000 nit is still somewhat on the low end, inparticular when having to deal with further display limitations likebacklight resolution. In experiments we found that definitely one can goto 10000 nit in dark viewing without the brightness getting superfluousor irritating (at least to some viewers). Going above 20000 nitpeak_white it may be a practical technical design consideration of whatto render true-to-life luminance-wise, and what to approximate, givingat least a brightness appearance. Note that one typically should notdrive such a bright display always at maximum brightness, rather to makean optimal HDR experience one should only use the brightest rendering atcertain places and times, conservatively, and also well-chosen as totheir temporal evolution. One should not only focus on intra-picture DR,but also on how different brightness environments are to be rendered insuccession, taking human visual adaptation into account.

Another dynamic range is the camera dynamic range CDR, which is just(given the exposure settings) determined by the full well of the pixel'sphotodiode, and the noise on the dark side. When using tricks likemultiple exposure or differently exposable pixel arrays (e.g. in 3 chipcameras), the CDR becomes limited by the optics (e.g. lens scattering,reflection on the lens or camera body, etc.), but also this can beimproved by suitable computational imaging techniques which try toseparate the real illumination from dark scene regions from erroneousirradiation due to stray light. Of course when the source of the imageis a computer graphics routine (like e.g. in special effects or a gamingapplication) one can easily create HDR far beyond those limitations. Wewill ignore the CDR, and just assume it is either very high, or perhapsa limiting factor but in a system which is supposed to handle situationsof very high originals. In particular, when we introduce clipping wewill assume it is not due to a low quality camera capturing, but due toa practical handling of some other limitations in the entire imagingchain, like the inability of a display to render very bright colors.

Now apart from the display environment RDR, which does actually generatethe right photon distribution to stimulate the viewer into the rightsensation (be that also dependent on the adaptation state of thatviewer), when talking about handling or coding HDR, there is anotherinteresting aspect, which can also be summarized in a dynamic range,which we shall call coding dynamic range CODR. A couple of thoughtexperiments should clarify this important concept. Suppose we were todraw on a bright back-illuminated white panel with a highly absorbingblack marker, so that we would get a transmission of 1/16000^(th) of thesurrounding white of the panel (and assuming the surrounding room andviewer are perfectly absorbing objects). In the linear bits world (bywhich we mean that we linearly represent all values between say 0 and2^B, where ^ is the power operation and B the number of bits) of e.g.the camera capturing (its ADC) we would hence need 14 bits forrepresenting this signal. However, as this codec would waste a lot ofcodes for values which don't occur anyway, we can say that to faithfullyrepresent that particular signal, we theoretically only need a 1-bitencoding. We would give black the code 0, and white a 1, and thenconvert them to whatever actual luminance they correspond to. Also notethat a display need not in fact render those values with exactly thesame luminances as in the scene. In fact, since this signal may look nobetter (psychologically and semantically) than a lower DR equivalentthereof (actually such a high contrast black and white drawing may evenlook weird), we might as well render it on a display with values 1 nitand 2000 nit. We see here for the first time an interesting distinctionwhich is important when talking about HDR encoding: the differencebetween physiological and psychological (or semantic) dynamic range.Human vision consists of two parts, the eye and the brain. The eye mayneed as a precursor the appropriate physiological dynamic range PDR toappropriately stimulate cones and/or rods (and thereby ganglion cellsetc.), but it is ultimately the brain that determines the final look ofthe image or scene (psychological dynamic range PSDR). Although itdoesn't quite give the exact impression of a very luminous region,painters like Petrus Van Schendel can play on the PSDR psychologicalprinciples to emulate in an LDR medium high dynamic range scenes likee.g. a fire in a dark night cityscape. This is also what complex gamutmapping algorithms try to do when preconditioning a HDR image forrendering on an LDR display. But the other side of this principle isthat some scenes will look more HDR-ish even on a HDR display thanothers (e.g. a sunny winter landscape with pale dried shrubs and sometrees in the back may look high brightness but not so HDR). For HDRactions, like e.g. turning a bright lamp towards the viewer,psychological emulations are usually not so convincing as the realbright rendering of the regions.

Consider along the same lines now a second example: we have an indoorsscene with luminances of say between 200 nit and 5 nit, and an outdoorsscene with luminances of say between 1500 and 20000 nit. This means thatagain we have two luminance histograms separated by non-existing codes.We may natively encode them on a range of say 16 linear bits (themaximum code e.g. corresponding to 32768 nit), although it would bepreferable to use some non-linearity to have enough accuracy in theblacks if there's not too much capturing noise. But we could also encodethis in a different way. E.g. we could sacrifice 1 bit of precision, anddivide an 8 bit nonlinear JPEG luma range in two adjacently touchingparts, the below one for the darker part of the above scene, and theupper one for the lighter (one may not want to cut exactly in the middlein view of the non-linear JND allocation). If one is concerned aboutloss of precise detail when having less bits, one may consider that itmay often be better to use available bits instead for HDR effects. Suchan allocation would typically correspond to a shifting and (non-linear)stretching of the luminance (L) values of the input RAW capturing to the8 bit luma (Y) values. Now one can again ask oneself the question ofwhat a dynamic range of such a scene is, if it can be “arbitrarily”compressed together or stretched apart (making the brighter outside evenbrighter, at least until this becomes e.g. unrealistic), at least inpost-processing for rendering. Here the concept of different appearancescan help out. We have in both subhistograms a number of differentluminance values for different pixels or regions, which assumedly aremostly or all relevant (if not, we don't need to encode them, and cane.g. drop one or more bits of precision). Also the separation (e.g.measured as a difference in average luminance) of the two histogramswhen ultimately rendered on a display has some appearance meaning. It isknown that human vision to some extent discounts the illumination, butnot entirely (especially if there are two brightness regions), so oneneeds to render/generate those eye inputs to at least a certain extent.So working with meaningful different color (or at least brightness orlightness) appearances of pixels or objects in a renderable scene (e.g.when rendered in the best possible display scenario) gives us an insightabout the coding dynamic range CODR, and how we hence need to encode HDRimages. If the image has many different appearances, it is HDR, andthose need to be present somehow in any reasonably faithful encoding.

Since classical image or video encoding technologies (e.g. PAL, JPEG,etc.) were primarily concerned with rendering mostly the object(reflection) lightnesses in a range of 100:1 under originally relativelyfixed viewing conditions (a CRT in a home environment, and not an OLEDin the train, or the same consumer having in its attic a dedicated darkcinema room with on-the-fly dynamically controllable lighting, which canadjust to the video content), those systems encoded the video in arather fixed way, in particular with a fixed universal master encodinggamma which mimics the brightness sensitivity of the eye, like e.g.V_709=1.099 L^0.45−0.099, which is approximately a square root function.However, such systems are not well-adapted to handle a vast range ofCODRs. In the last couple of years there have been attempts to encodeHDR, either in a native way of scene-referred linearly encoding allpossible input luminances, like in the OpenEXR system (F. Kainz and R.Bogart: http://www.openexr.com/TechnicalIntroduction.pdf). Or, there are2-layer systems based on the classical scalability philosophy. Theseneed at least two images: a base image which will typically be alegacy-usable LDR image, and an image to reconstruct the master HDRimage(s). An example of such is US2012/0314944, which needs the LDRimage, a logarithmic boost or ratio image (obtained by dividing the HDRluminances by the LDR luminances obtained after suitably grading an LDRimage for LDR rendering systems), and a color clipping correction imageper HDR to-be-encoded image. With a boost image one can boost allregions (depending on subsampling) from their limited range to whateverluminance-position they should occupy on the HDR range. Note that forsimplicity we describe all such operations in a luminance view, sincethe skilled person can imagine how those should be formulated in a lumaview of a particular encoding definition. Such multi-images are at leastthe coming years somewhat cumbersome since they need seriously upgraded(de)coding ICs in existing apparatuses, since the handling of furtherimages in addition to the LDR image is required.

Recently and as described in WO2013/046095 we have developed a way toimprove the classical video encoding (preferably with minormodifications, preferably with mostly metadata to apply transformationsrelating two gradings of the same scene for two very different renderingconditions, such as e.g. allowing to transform an encoded LDR grading ina HDR grading or vice versa, and perhaps with some variants having roomto store in the metadata a couple of additional small pictures to do afinal tuning if such a further modification is desired, e.g. an additiveor multiplicative correction on a small regions containing an objectlike e.g. a very brightly illuminated face in one shot or scene of themovie, in which the corrective factors per pixels may then be encodede.g. in 200 120×60 pixel images to be mapped onto the pixel positions ofthe current HDR reconstruction by color transformation, or even somesubsampled representation of those small corrective images, to beapplied as coarse finetuning mappings, described as images) to be ableto encode high dynamic range images. In this system typically a humangrader can determine an optimal mapping function from the input HDRimage (master HDR grading) to a e.g. 8 or 10 (or 12 or in principleanother value for at least the luma codes, but this value beingtypically what is reserved for “classical” LDR image encoding) bit LDRencoding which can be encoded through classical video compression (DCTetc.), the optimal mapping function (e.g. a gamma function or similarwith optimal gamma coefficient, linear part etc., or a multisegmentfunction like e.g. an S-curve etc.) typically depending on what thecontent in the master HDR was (e.g. a dark background, with a verybrightly lit region), and how it will be rendered in LDR conditions. Wecall this simultaneous encoding of an LDR and HDR grading by mapping theHDR grading into a legacy-usable LDR image and LDR-container encoding ofHDR. We wanted to make sure in this technology, that it was backwardscompatible, in that the so-generated LDR image gives reasonable resultswhen rendered on a e.g. legacy LDR system (i.e. the picture looksreasonably nice, if not perfect typically not so that too many peoplewill consider the colors of some objects all wrong). If one acceptssomewhat of a diminuation of precision, our system can even encode HDRscenes or effects on legacy 8 bit systems. With reasonable results wemean that the LDR rendered images, although perhaps not the best onetheoretically could achieve artistic look-wise, will be acceptable to acontent creator and/or viewer, this depending of course on theapplication (e.g. for a cheaper internet-based or mobile service qualityconstraints may be less critical). At least the LDR grading will givegood visibility of most or all objects (at least the objects of mainimportance for the story of the image or video) in the imaged scene whenrendered in an LDR system of properties not deviating much fromstandardized LDR rendering. On the other hand, for HDR displays, theoriginal master HDR can be approximated in a close approximation bymapping with the invertible reverse of the co-encoded mapping functionfrom the LDR image to the reconstructed HDR image. One can define suchan approximation with mathematical tolerance, e.g. in terms of justnoticeable differences (JNDs) between the original master HDR inputed,and its reconstruction. Typically one will design any such a system bytesting for a number of typical HDR scenes, actions, and furthersituations how much different the reconstructed HDR looks (if that isstill acceptable for certain classes of users, like e.g. television ormovie content creators) and validate a class of operations likeparticular gamma mappings within certain parameter ranges therefrom.This warrants that always a certain quality of the approximation can beachieved.

It is an object of the below presented technologies to give the gradereven more versatility in defining at least two gradings, LDR and HDR.

SUMMARY OF THE INVENTION

The above object is realized by having an image encoder (202)comprising:

-   -   an input (240) for a high dynamic range input image (M_HDR);    -   an image grading unit (201) arranged to allow a human color        grader to specify a color mapping from a representation        (HDR_REP) of the high dynamic range input image defined        according to a predefined accuracy, to a low dynamic range image        (Im_LDR) by means of a human-determined color mapping algorithm,        and arranged to output data specifying the color mapping        (Fi(MP_DH)); and    -   an automatic grading unit (203) arranged to derive a second low        dynamic range image (GT_IDR) by applying an automatic color        mapping algorithm to one of the high dynamic range input image        (M_HDR) or the low dynamic range image (Im_LDR).

One will typically make the GT_IDR grading from either the high dynamicrange image (typically master grading) or the LDR grading, but of courseit may also be advantageous to take into account therewith the imagecharacteristics, in particular the brightness or lightness look ofvarious object, of the other grading (i.e. what the LDR grading shouldlook like if we map GT_IDR from M_HDR, so that the GT_IDR may be somekind of balance, but of course it may be formed by all kinds of otherside conditions/factors too). We assume that the master HDR image isencoded in any format allowing such an encoding (e.g. this may beOpenEXR, or a system as elucidated with our FIG. 7, in general anythingpreferred by e.g. the maker of the grading software). I.e. the M_HDRencoding may be of a linear, scene-referred type, or already have someinteresting code allocation function applied to it, but for ourexplanations we could safely assume it to be a linear luminanceencoding. Typically such a master HDR image will not be straight fromcamera (as cameras being just automatic capturing tools, withcharacteristics like e.g. color filters not like to human eye, but moreimportantly their circuits not like the human brain, what comes out ofthem by mere recording may be good but not necessarily optimal), but anartistic optimal grading (which e.g. darkens a basement backgroundenvironment to create an optimal mood for that scene), however, thehuman grading could be a simple functional mapping of the image of acamera somewhere (this then being the master HDR input), e.g. just toget a first view on a certain renderer, after which a high quality HDRimage is encoded (via an LDR image and mapping parameters). An imagegrading unit is typically software running on a computer, which allowscolor mapping from initial colors of pixels to final colors of pixels,e.g. changing a luminance-correlate of those pixels from an initial to afinal value by e.g. applying a tone mapping function (e.g. an S-curve)on that luminance-correlate or e.g. color defining curves (like R,G,B)simultaneously. The skilled person should understand why we use the termluminance-correlate to denote any mathematical encoding correlating witha luminance of a pixel (when captured in a scene, or rendered on arendering thereof), since given the complexity of color technologies,there exist several similar variants thereof, like lumas, values (V),functional definitions for correlates called lightness, etc. In fact alinear or non-linear component of the color, like an amount of red, canalso be used as a luminance-correlate. So luminance-correlate should beunderstood as any monotonous mapping function between the luminance axis(luminance as defined by the CIE) and another axis, so that any value onthat other axis can immediately be converted into a luminance value andvice versa. Although the formulations for various correlates vary intheir precise details the principle stays the same. But we introducedthe term also to indicate that although the principles of ourembodiments can be defined on luminance mappings, they may actually bephysically constructed by doing mathematics on otherluminance-correlates, or in general any color encoding. The human colorgrader may e.g. be directed partially by the director of a movie toproduce a certain look for the captured movie.

The principle of the above embodiment is that, contrary to legacysystems with a fixed mapping function relating an LDR and HDR grade(e.g. encoding any input image, whether it has an increased dynamicrange or not, into an LDR encoding), we now have a dual system. Thiswill typically also create two sets of mapping parameters (e.g.luminance-correlate mapping functions, or in general data definingsoftware-realized mathematical transforms to transform the input colorsin the output colors) instead of only one reversible function to createthe to be rendered image (in case of PCT/2012/054984 an HDRreconstruction to be used on an HDR display for driving it, directly orafter further color processing).

In this dual system there are also two gradings relatable to the masterHDR. Firstly, there is an automatic grading, which creates a goodquality first LDR image, which we call the second low dynamic rangeimage GT_IDR. It may be (partially) influenced by the color grader (e.g.by selecting a preferred one out of a set of mapping function whichnormally would yield good results on all input images), but typically itis advantageous if this automatic grading appears behind the scenes inthe apparatus, out of view and concern of the grader who can then focuson his artistic desires. The point of this technical grading is tocreate an image GT_IDR which, although perhaps not fully optimalaccording to the grader's particular artistic desires, produces awell-viewable LDR image when rendered on an LDR system (well-viewableagain meaning not just that any image will come out, but that the viewercan follow most of what is happening in the movie, because visibility ofall objects is good, although due to some discoloration compared to theoptimum the mood of the scene may be altered somewhat). But mostly itdefines its mathematical derivation so that this second LDR image GT_IDRis technically optimal, in that it is easy to reconstruct from it areconstruction REC_HDR of the master HDR with optimal quality. Thismeans that the information loss in GT_IDR due to e.g. quantization afterusing the particular optimal mapping to it from M_HDR should be minimal,so that there is a minimal acceptable amount in the reconstructed HDRfor all typical possible HDR input images.

Then on the other hand there is an artistic grading of the human grader.He can derive whatever picture he wants according to his preferences tosee for LDR rendering systems. E.g., we may have an action happening ina dark basement in a horror movie. The HDR rendering system may be ableto render the dark surroundings very dark while still retaining thevisibility of most objects (e.g. torturing equipment on shelves in theshadows against the wall, or the interior of an unlit adjacent roombehind an open door). And at the same time it may be able to render verybright objects, like a single light bulb oscillating on the ceiling ofthat dark room, or a torch in the hands of a person walking through it.However, the LDR rendering system may have lesser capabilities forrendering the dark surroundings, in particular because it also needs tomake room in its limited luminance range for the brighter objects likethe light bulb and the face of the person walking under it, and thegrader may want to emulate the brighteness by increasing the contrastwith the luminances of the surrounding objects, i.e. the darkbackground. The grader may e.g. artistically decide to make thisbackground entirely black for the LDR grading Im_HDR. It should be clearthat this low dynamic range image Im_HDR can then not be used forreconstructing a REC_HDR with enough information in the background tohave all objects there visible. As a generalization of this, it can beseen that the automatic grading unit must make sure no relevantinformation loss occurs, so that a HDR reconstruction can still bederived with good approximation accuracy from the GT_IDR encoded LDRimage. Note that this LDR image GT_IDR need not perse be defined withthe same environmental constraints (e.g. 100 nit peak_white of intendeddisplay), but it may also be e.g. for a 200 nit reference display.

As the skilled person can understand, there are two ways to realize sucha system. Either the technically grading automatic grading unit does itsmapping first, and then the human grader works on that GT_IDR to createhis preferred LDR grading IM_LDR, or the human grader first does hisgrading Im_LDR, and then the automatic grading unit derives therefrom atechnically more suitable GT_IDR for encoding all relevant HDR data inan LDR_container format. So in fact this logically corresponds to thatthe human grader will in both cases work on representation of the masterHDR. In the human grading first case it will be the (infinitelyaccurate) master HDR itself which forms the starting point. In thesecond case the resultant GT_IDR from the automatic technical gradingwill be a good representation of the master HDR, since it contains most(at least the relevant) data of the master HDR, be it in a mapped,different luminance-correlate representation (e.g. a luminance of a lampof 10000 nit in the master HDR may be represented as a luma code 253 inthe GT_IDR). According to a predefined accuracy again means that oneputs technical bounds on how much a reconstruction REC_HDR from theGT_IDR may deviate from the originally inputted M_HDR. Typically theskilled person knows one can (if not solely defined according to humanpanel preference) mathematically characterize such deviations e.g. bymeans of weighted difference between the pixel colors of the REC_HDR andthe M_HDR. E.g., one may use mathematical functions which characterize adifference following human visual principles, e.g. looking at colors inregions, and e.g. penalizing differences less if they occur in texturedareas, etc. One can allow larger differences to some semantical objectslike e.g. lamps, since the actual rendered luminance for those objectsmay be less important. In summary the skilled person will understandthat typically the technical grading will do any of a set of pre-agreedmappings, which for any or most of the typically occurring input M_HDRimages will yield reconstruction errors below a certain threshold (whichcan either be a subjective agreed value by the human evaluation panel,or an agreed mathematical value). Typically the will be e.g. a set ofgamma-like functions (i.e. typically starting with a linear part in theblacks, and then bending to show ever decreasing slope of the outputversus the input), or parametric three-segment curves for affecting thedarks/shadows, mids and brights subranges of the luminance orluminance-correlate axis, which all behave reasonably, and some may givelesser reconstruction errors in a particular luminance region of aparticular type of M_HDR. The human grader may then select such a curve.Or alternatively, the automatic grading unit may select such an optimalcurve by e.g. looking at the color or luminance histogram of the M_HDR,or doing a more complicated analysis thereon (e.g. determining where theface(s) are). So representation HDR_REP of the M_HDR according to apredefined accuracy means that this image contains substantially all thedata of M_HDR, be it in a differently encoded way, so that one canreversly re-obtain the inputed M_HDR within a predefined accuracy, i.e.with reconstruction errors worst-case typically not exceeding an agreedlevel.

So the human grader hence works either on the M_HDR, or on the GT_IDR toobtain his preferred low dynamic range image Im_LDR to be used for LDRrendering systems. He may use any color mapping from a set of availablemappings in the grading software he decides, e.g. he may taylor aspecific global tone mapping (i.e. luminance mapping) or color mappingfunction to be applied on all pixels at whatever spatial position in theimage based solely on their input color value. Or he may use locallyfinetuned mappings. E.g. he may in a particular geometrical region ofthe image (e.g. specified within a rectangular or otherwise definedbounding shape) select only those pixels which are brighter than aparticular luma value (or within a ranges of specified colors) andtransform only those pixels according to a local color mapping strategy,etc. He will then write all the things he did as metadata, e.g. theglobal luminance-correlate changing function can be written in aparametric form (e.g. power function coefficients for three regions ofan S-like curve, like and end point of the shadows, linear parts oneither side, a parabolic curvature coefficient, etc.). If thesefunctions are (largely) reversible, the receiving side can then usethose to reconstruct back by using this output image as an input imageand using the inverse color mapping strategy, the original image thatthis output image was obtained from, at least within a certain accuracy(after e.g. quantization and/or DCT artefacts etc. have beenintroduced).

In the human grading first embodiments, the human grader will producethe mapping parameters Fi(MP_DH) from the M_HDR mapping. However, sincethe automatic grading will still modify the LDR grading, these are notthe interesting parameters in the end. The automatic grading unit willderive therefrom two sets of new parameters. It will derive a differentmapping from HDR to the new LDR grading being GT_IDR, with mappingparameters Fi(MP_T). It will also derive new mapping parametersFi(MP_DL) to create the human-preferred LDR grading Im_LDR from thetechnically graded second LDR image GT_IDR. When storing the data neededfor a receiving side to work on the encoded M_HDR image, i.e. inparticular allowing the receiver to recreate a reconstruction REC_HDR, aformatter will typically encode the GT_IDR (for the texture of theobjects), and two sets of mapping data Fi(MP_T) and Fi(MP_DL), into asuitable encoding defined in the specification of any signal standard,i.e. typically in metadata of the image (or video) signal TSIG. In caseof the automatic grading first, the human grader will work on GT_IDR toproduce mapping parameters Fi(MP_DL), and then these will be writteninto the signal (in addition to the GT_IDR image and Fi(MP_T)).

Depending on which variant the system is, the automatic grading unitwill then either apply as a prespecification the second LDR image GT_IDRdirectly from the master HDR M_HDR, or as a postcorrection based upon apriorly human graded Im_LDR as input. The term data specifying a colormapping should be clear to the skilled person for any of the manypossible variants of color mapping. Typically the grading software maystore the parameters of the functions it uses, and in particular it mayuse mapping functions which are preconditioned to be good for encoding.E.g. we can design a number of local or global functions which arereversible (within a specified accuracy) when used conservatively, i.e.with values within a range, and may become (partially) irreversible whenused aggressively by the grader. An example of such may be a gammafunction. Gamma coefficients up to a value of 3.0 may be seen asreversible for a particular system (i.e. going from a particular initialdynamic range, e.g. CODR, or e.g. with significant important data inseveral subranges of a 5000 nit-defined reference range, to a particularLDR reference situation, e.g. a legacy LDR system definition e.g. theviewing environment specification of sRGB), but gammas above 3.0 may beseen as to severe for at least a subrange of the input luminance range(i.e. for reversible reconstruction). Or in an extended set to produce aposteriori LDR gradings from the automatic GT_IDR, there may befunctions which do not exist in the automatic grading, and createsignificant information loss on the input HDR information upon creatinga desired graded LDR image therewith. The system may typically work in amode or phase were the grader has a limited freedom to create LDRimages, but with good technical properties (i.e. close to awell-functioning GT_IDR), and a mode or phase in which the grader has(near) unlimited freedom, or at least greater freedom in determining hisoptimal LDR graded image Im_LDR.

In advantageous embodiments the automatic grading unit (203) is arrangedto determine its automatic color mapping algorithm by fulfilling acondition that a HDR reconstructed image (REC_HDR) falling within asecond predefined accuracy from the high dynamic range input image(M_HDR) can be calculated by applying a second color mapping algorithm(CMAP_2) to the second low dynamic range image (GT_IDR).

So the automatic grading unit will maintain the quality of the secondLDR image GT_IDR to enable good reconstruction of the master HDR. Itwill fulfill this condition by constraining the functions that can beused to relate the M_HDR with the GT_IDR. In particular, not too much(significant) data should be lost by such acts as e.g. quantization ofcolor components like e.g. (R,G,B) or (Y,Cr,Cb), etc. So it willtypically select its mapping functions based on such an evaluation,whether this was an a priori calculation (e.g. by an algorithm which ispretested in the lab so that when it operates on HDR images with e.g.certain luminance histogram distribution properties it will yield goodreconstructability for certain mapping functions or algorithms), or witha post-calculation, e.g. in an iterative loop selecting the best of anumber of possible mapping functions. The second predetermined accuracyis the final accuracy achievable by reconstructing the REC_HDR from thedata encoded with the chosen mapping algorithm, i.e. by applying theinverse of Fi(MP_T) on the GT_IDR, which inverse we call second colormapping algorithm CMAP_2. For the automatic grading first embodimentsthis will mean that the automatic grading unit will solely determine themapping between M_HDR and GT_IDR (and the user need in fact not bebothered with that relation). It will then select e.g. an appropriategamma function, so that the GT_IDR still has a reasonable approximationto the darkish look in the M_HDR, yet, none of the relevant luminancevalues are clustered together in one luma of GT_IDR too much. In thesituation of the human-first grading, the automatic grading unit stillhas to determine a final mapping Fi(MP_T) between M_HDR and GT_IDR. Thiscorresponds to redetermining a new second LDR graded image GT_IDR afterthe human grader (but this will not destroy the human grading, sincemapping parameters for reconstructing it from GT_IDR are alsodetermined). Several strategies can exist for that. E.g., the automaticgrading unit can look at the mapping function, and deviate it somewhatin regions which lead to severe data loss, e.g. due to quantization.Thereto the automatic grading unit could study the obtained images(Im_LDR vs. GT_IDR as compared to M_HDR) but also the mapping curveitself (by seeing how much it deviates from generically well-performingmapping curves). Another possibility is that the automatic grading unitselects one of a set of mapping functions which is close to the oneselected by the human grader, yet well-performing. From thereon it ismathematical calculation to obtain the final system. E.g. GT_IDR will beobtained by applying a deviation function on the human gradersM_HDR-to-Im_LDR mapping function Fi(MP_DH). Actually, the automaticgrading unit can then apply this final function directly to M_HDR toobtain GT_IDR, directly with minimal error. Im_LDR can be derivedtherefrom by using the deviation function. The skilled personunderstands how similarly in other mathematical frameworks the automaticgrading unit can determine an optimal mapping Fi(MP_T) and correspondingtherewith a mapping from the GT_IDR to the grader's Im_LDR (i.e.Fi(MP_DL)). We have schematically shown this in FIG. 6 as applying atechnical deformation DEF_TECH to the grading of the human grader, toobtain the technically graded LDR image GT_IDR. I.e. the automaticgrading unit can work either starting from the LDR image Im_LDR and workin a deformation philosophy, and derive therefrom Fi(MP_T), or it candirectly look at the look of the human grading Im_LDR, and make anapproximation thereof starting from M_HDR, given the technicallimitations of its technical grading, leading to a Fi(MP_T), anddetermine therefrom an Fi(MP_DL) to derive the human grading from GT_IDR(which mapping may then be very liberal technically), etc. So it shouldbe clear for the skilled person in which ways the condition can and willbe fulfilled. Again the accuracy can be predefined as any measure, e.g.for a quality class of technology (e.g. high quality movie for premiumusers vs. low quality HDR encoding giving mostly the impression, but notthe ultimate quality), e.g. specifying that certain mappings will ondifficult case HDR image create artefacts which are no larger thanartefacts of a pre-agreed magnitude. Other mapping strategies which donot behave according to specification should then not be used. In anycase, apart from minutely accurate details in definitions, it should beclear for any infringer whether he is using the dual grading technicalchain system as described above.

As already introduced above, it may be advantageous if at least theautomatic grading unit (203), and possibly also the image grading unit(201), are arranged to apply a monotonous mapping function on at least aluminance-correlate of pixels in their respective input image, in atleast a geometrical region of the respective input image correspondingto a same geometrical region of the high dynamic range input image(M_HDR). Having such a one-to-one functional definition in uniquelyidentifiable regions of the image (e.g. the entire image), means that atleast on an infinite precision axis one can easily invert thesefunctions. It is especially advantageous if also the derivatives orslopes of these functions are so that they do not merge many of theM_HDR luminances into a single code of Im_LDR or at least GT_IDR. Alsosuch monotonous functions are easy to calculate technically, e.g. with alookup table. E.g. this may take a luminance-correlate such as a luma Yas input and output. An example of an often-occurring HDR scene whichcan be done with two spatial regions is an inside-outside image, e.g.photographed from inside a car, or room, etc. With geometrical regionscorresponding to we mean that if the region is defined on say theIm_LDR, then the pixels are identifiable with pixels in M_HDR. E.g. ifthe image has the same geometry (resolution and cut), the pixelpositions may collocate, but in case of geometrical transformations likee.g. scalings it should also be clear what is meant to the skilledperson.

Although simple systems may e.g. use fixed, pre-agreed, always correctlyfunctioning mapping functions Fi(MP_T), it is advantageous if moreadvanced systems can optimally determine mappings themselves, inparticular if the automatic grading unit (203) is arranged to determineits automatic color mapping algorithm in accordance with a qualitycriterion that estimates a difference of an amount of information in theluminance-correlates of pixels in the high dynamic range input image(M_HDR) and an amount of information in the luminance-correlates ofpixels in the second low dynamic range image (GT_IDR).

The skilled person will understand there are different ways to defineamounts of information, but they all involve measuring how much datathere is in a representation (especially meaningful data). There may besemantically-blind methods, which only measure the available colors, butnot which region or object they come from. E.g., one may measure howmany of the luminances of the M_HDR map to a single luma of GT_IDR. Ife.g. most luminances map only two-by-two, but in a certain region of theM_HDR luminance axis 5 digital values of HDR's luminance (or in afloating representation a span of luminances exceeding a certain size)map to a single GT_IDR luma, this may be seen as a too large informationloss. So the size of spans, or amount of digitized luminances in M_HDRis an example of a possible amount of information measures. Of coursethese measures can be made more smart, by e.g. looking a how they behaveover particular interesting subregions of the M_HDR luminance range, oreven semantic object like e.g. a face. It can be prescribed that e.g.each face should be represented by at least 50 luma codes in GT_IDR, oreach region of a face having N digital luminances in M_HDR (or acontinuous span equivalent thereof) shall not be represented in GT_IDRby an amount M of lumas of less than half of that amount N. This can befinetuned based on non-linear meaningfulness for humans given thenon-linear mapping function. E.g., one can specify how many justnoticeable differences JNDs a certain coding GT_IDR when reconstructedto REC_HDR under a reference HDR viewing environment would correspondto. And then one can specify that the face should be reconstructablewith at least R discriminable JNDs. Or a structure in a face like awrinkle should change from a darker value inside the wrinkle to abrighter value outside the wrinkle by a reconstructable step ofmaximally S (say 3) JNDs. We also introduce the concept just careabledifferences JCDs which can be used for some semantic objects. E.g. in alamp, it may be sufficient that the lamp is bright, and still somethingof the interior structure (like a bulb shape) is discernable, butneither the exact value of the lamp, nor of the bulb, nor there relativeluminances may be critical. In that case both regions may be encoded asconsidered precise if within e.g. 1 JCD, which may be e.g. 20 JNDs, orspecified as a difference or fraction of luminances (for luminancesfalling in a defined subrange of bright luminances to be used forrendering lights). So the information criterion may be determined basedonly on one- or three-dimensional binning (shape and/or size) of colordata in both images, on statistical criteria like the luminance or colorhistogram and in particular semantical information of which regions maybe more severely deformed (e.g. the human grader can quickly draw ascribble on image regions which have to be encoded with high precisionlike the main-region of action, which may be specially lit duringcapturing, or a face), geometrical information, like e.g. edges orshapes of structures in object regions and how they deform (e.g. clearvisibility, or contrast) under certain classes of mappings, or texturecharacterizers (e.g. in complex textures a greater amount of artefactsis allowable), or semantical information like automatic detection ofparticular objects, or the human-characterization thereof (by at leastroughly marking a region and ac class like “less critical lamp”), etc.So the skilled person can understand there can be various ways topredefine a system of mathematical functions which specify when too muchdata has been lost, e.g. reducing the quality of a texture-less varyingillumination over an object, etc. There may be one single criterion, ora set of criteria which results in a full analysis of the image GT_IDR,and mark that a certain region thereof has to be redone. With thisinformation the image grading unit can determine whether a mappingsatisfies the technical requirement, or can determine a new mapping,e.g. by slightly adjusting the old one. E.g. in case one region of theGT_IDR still reconstructs one region (e.g. an object) of M_HDR toocoarsely, the image grading unit can either fully redetermine e.g. aglobal mapping (typically of course it may only finetune the mapping forthose M_HDR luminance regions which pose a problem, e.g. it may increasethe derivative of the Fi(MP_T) downwards mapping function for theproblematic luminance subrange, which typically corresponds to outwardsshifting—respectively to darker resp. brighter values—of the other pixelcolors, and adjusting to the new available range for them by a softbending of those parts of the mapping function). Or the image gradingunit can derive an additional local grading to be applied in temporalsuccession, e.g. a preboosting of that region, and saving in aco-encoded partial (correction) image, etc. Typically it is advantageouswhen the image grading unit, even when it creates GT_IDR withpre-acknowledged suitable color mapping strategy, post-determines whenthe GT_IDR image indeed satisfies the condition that REC_HDR is anapproximation of sufficient quality.

Advantageously the automatic grading unit (203) is arranged to determinethe monotonous mapping function (Fi(MP_T)) from luminance-correlates ofpixels of the high dynamic range input image (M_HDR) toluminance-correlates of pixels of the second low dynamic range image(GT_IDR) according to a criterion which determines respective ranges ofluminance-correlates of pixels of the high dynamic range input image(M_HDR) allocated to respective single values of a luminance-correlateof pixels of the second low dynamic range image (GT_IDR), the respectiveranges forming a set of luminance-correlate ranges covering the totalrange of possible luminance-correlate values for the high dynamic rangeinput image (M_HDR). This is a simple way to determine loss ofinformation, e.g. due to excessive quantization. E.g., a predefined sizeof range to map on a single value versus M_HDR input luminance along theM_HDR luminance axis of all possible values may be defined, which allowsfor specifying that the brighter objects may be quantized more coarsely.It may be that they are already approximated with significant errorcompared to the original captured scene (e.g. one need not render carlights exactly with 100,000 nits on the HDR display), so one mightaccept an additional error in the REC_HDR. This criterion can then beeasily converted into e.g. determining a shape of a mapping function,since it should in no place bend so strong as to map a greater rangethan allowed to a single quantized value, giving the known settings ofthe GT_IDR coder (e.g. MPEG2 quantization values).

The above describes the inner workings of an encoder which can be usedin various apparatuses, like e.g. an intermediate system in an imagecalculation unit, but it is advantageous if the obtained encoded data issent outside, e.g. as a signal which can be used by a receiver, i.e. theimage encoder (202) comprises a data formatter (220) arranged to outputinto an image signal (TSIG) the second low dynamic range image (GT_IDR)and at least one of, or both of, data describing the color mapping(Fi(MP_T)) between the high dynamic range input image (M_HDR) and thesecond low dynamic range image (GT_IDR), and data describing the colormapping (Fi(MP_DL)) between the low dynamic range image (Im_LDR) and thesecond low dynamic range image (GT_IDR). In principle not all receiverswould need both sets of parameters, but it is advantageous if a receivergets both, and can then e.g. optimally determine how to use allavailable information to come to a final driving signal for a particulardisplay and viewing environment (e.g. it could mix information of theencoded HDR and LDR grading, to arrive at a new grading, which we calldisplay tunability). Note that although we described our basic systemwith only two gradings, in the same system there may be furthergradings, e.g. a second HDR grading for ultrabright HDR display, or athird LDR grading, or a grading for an MDR display (of an intermediatepeak_white between say 100 nit and 5000 nit references of the LDR resp.HDR grades), or a grading for sub_LDR displays, and these may beconstructed as independently designed add-ons, but also according to thepresented inventive philosophies, e.g. one can derive a second technicalgrading GT_IDR2, which is an HDR technically derived grading from theM_HDR and serves for defining the ultraHDR gradings. E.g. the GT_IDR2can be derived by simple mathematical stretching of the brightest lightregions, but the grader can correct upon this by defining furthermapping data Fi(MP_DHH) e.g. for correcting by mapping from GT_IDR2.

The image encoder corresponds to an image decoder (401) arranged toreceive via an image signal input (405) an image signal comprising asecond low dynamic range image (GT_IDR), and data describing a firstcolor mapping (Fi(MP_T)) enabling reconstruction of a reconstruction(REC_HDR) of a high dynamic range image (M_HDR) on the basis of thesecond low dynamic range image (GT_IDR), and data describing a secondcolor mapping (Fi(MP_DL)) allowing calculation of a low dynamic rangeimage (Im_LDR) on the basis of the second low dynamic range image(GT_IDR), the image decoder comprising an image derivation unit (403)arranged to derive at least the low dynamic range image (Im_LDR) on thebasis of the data describing the second color mapping (Fi(MP_DL)) andthe pixel colors encoded in the second low dynamic range image (GT_IDR).One will see from this decoder that it can access mapping parameters tocolor map an LDR image both upwards, to a REC_HDR and “downwards” toobtain a content-creator desirable LDR grading Im_LDR. The imagederivation unit will have functionality (e.g. loaded software orhardware parts of an IC) to perform the required (e.g. pre-agreed)decoding color mappings. One can also see that the technical gradeGT_IDR is a technical grade, since it will have less mood (even withoutcomparing with the optimal Im_LDR) as the luminances of the objects arenot in the optimal place along the luminance axis, will typically havesome lesser contrast, somewhat brighter darks, etc., and of course alimited amount of codes for the various object regions in the image.

Advantageously the image decoder (401) comprises a system configurationunit (402), arranged to determine whether the decoder is connected toand/or supposed to derive an image for at least one of a high dynamicrange display (411) and a low dynamic range display (416), and thesystem configuration unit (402) being arranged to configure the imagederivation unit (403) to determine at least the reconstruction (REC_HDR)in case of a connection to the high dynamic range display (411), andarranged to configure the image derivation unit (403) to determine atleast the low dynamic range image (Im_LDR) in case of a connection tothe low dynamic range display (416). Our system (i.e. encoded signal,and various types of decoder) must be able to work with simple decoderswhich e.g. receive an HDR encoded as our above LDR_container in GT_IDR,but need from this only the LDR for an LDR display. They will thenignore most of the information, and only extract GT_IDR and FI(MP_DL),and calculate Im_LDR therefrom. More sophisticated decoders will e.g.determine on the fly with display they are connected, e.g. wirelessly,and supply various combinations of all the received encoded informationoptimally to the various connected displays (e.g. same movie to parentsin attic cinema room, and to child in bed watching on his LDR portable).

So advantageously the image decoder (401) as claimed in any of the aboveclaims having as an output a wired connection (410) or a wirelessconnection (415) to any connectable display, and a signal formatter(407) arranged to transmit at least one or both of the reconstruction(REC_HDR) and the low dynamic range image (Im_LDR) to any connecteddisplay.

Also advantageously the image derivation unit (403) is arranged todetermine a further image based on the reconstruction (REC_HDR) and thelow dynamic range image (Im_LDR), or the second low dynamic range image(GT_IDR) and data describing the first color mapping (Fi(MP_T)) and datadescribing the second color mapping (Fi(MP_DL)). This allows determiningoptimal final grades (e.g. direct driving signals) for various connecteddisplays (display tunability, e.g. getting via a measurement of thedisplay a value of the surround illumination etc., and optimizingtherewith the display driving signal).

The image encoder may be comprised in various apparatuses, e.g. itsimage signal input (405) may be connected to a reading unit (409)arranged to read the image signal from a memory object (102), such ase.g. a blu-ray disk.

All embodiments of the above apparatuses may be further realized asequivalent methods, signals, signal-storing products, in various uses orapplications, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the method and apparatus according to theinvention will be apparent from and elucidated with reference to theimplementations and embodiments described hereinafter, and withreference to the accompanying drawings, which serve merely asnon-limiting specific illustrations exemplifying the more generalconcept, and in which dashes are used to indicate that a component isoptional, non-dashed components not necessarily being essential. Dashescan also be used for indicating that elements, which are explained to beessential, are hidden in the interior of an object, or for intangiblethings such as e.g. selections of objects/regions (and how they may beshown on a display).

In the drawings:

FIG. 1 schematically illustrates a legacy image or video encodingsystem, as exemplified with a camera with tunable kneepoint;

FIG. 2 schematically illustrates a first possible realization of ourimage coding system, in which the automatic grading unit derives theautomatically graded second LDR image GT_IDR on the basis of a priorhuman LDR grading;

FIG. 3 schematically illustrates a second possible realization of ourimage coding system in which an automatically graded second LDR imageGT_IDR from an automatic grading unit serves as a basis for by furthergrading defining a final LDR grading Im_LDR by a human grader;

FIG. 4 schematically illustrates a possible variant of an image decodingsystem, in which a high end decoding apparatus reads the coded dataaccording to any of our coding embodiments, and derives therefromappropriate signals for various different connected displays;

FIG. 5 schematically illustrates an encoder embodiment according to theprinciples of our invention being incorporated in a camera;

FIG. 6 schematically illustrates a principle behind a variant of ourencoding, shown as a logical graph of color mapping relationshipsbetween gradings;

FIG. 7 schematically illustrates a way to define our starting inputwhich is a master HDR grading M_HDR, and shows how data from a camera orcomputer graphics system can be written into such a mathematical colorspecification, in particular along the range of its luminance-correlate;

FIG. 8 schematically illustrates an example of a color mapping strategy,namely a luminance mapping part thereof;

FIG. 9 schematically illustrates an example of how to determine whetherany mapping function or algorithm has suitable accuracy forreconstruction of the REC_HDR;

FIG. 10 schematically illustrates an example of how to transform anunsuitable function into a suitable one;

FIG. 11 schematically illustrates some examples of how to handle thecolor mapping in 3D color space;

FIG. 12b schematically illustrates how a grader can interact with a codeallocation curve to finetune it, and in FIG. 12a it is schematicallyshown how regions of the code allocation curve can be selected by(co)-interaction with the objects in the currently viewed image;

FIG. 13 schematically illustrates how one can go from a technicalgrading which in this example would be usable for LDR rendering already,to a better looking LDR grading, by a very simple parametrictransformation realizing a good quality content-adaptive contraststretching;

FIG. 14 gives an example of how one can handle further colorimetriclimitations in the technical grading, which can upon receipt then beused when generating the optimal image for rendering on a particulardisplay;

FIG. 15 schematically illustrates a useful new strategy for saturationprocessing, which is especially interesting for grading to colorrepresentations having a different luminance structure, such as e.g.because of an intended rendering on a display of a different luminancedynamic range;

FIG. 16 schematically illustrates a creation and usage part apparatusfor such novel saturation processing; and

FIG. 17 schematically illustrates just two possible uses when gradingsfor a higher and lower dynamic range rendering situation are required.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 shortly summarizes the ideas behind all classical image and videoencodings, which we call LDR encoding. Suppose we have say aprofessional television camera (although similar considerations apply toconsumer still cameras) capturing light with an image sensor 104, e.g. aCMOS sensor. This light will after an ADC be in a linear space whichcorrelates with luminance (in principle being luminance times a scalefactor when ignoring noise, ADC non-linearities, etc.), and will e.g. bean R,G,B so-called raw signal (or Cyan, Magenta, Yellow, Green orsimilar for other sensors but these will then be matrixed to RGB, so wecan focus on that). The principle of this LDR capturing is that a signalshould look good on an home television (which used to be a CRT ofapproximately 100 nit peak_white, or somewhat darker or brighter). In astudio a director, camera operator or similar person will directly watchthe output of the camera on a reference CRT, to check whether thecaptured program does indeed look good. The capturing of such an LDRprogram (in fact the automatic exposure as primarily determined byselecting an aperture setting) is determined by the principle of havingan appropriate rendered brightness for middle gray. As this middle greyis via the non-linearity of human vision directly linked the whites insimilarly illuminated parts of the scene (and assuming (near)-linearreproduction, up to a scale factor typically, on the CRT also), namelyit corresponds to objects reflecting approximately 18% of the infallinglight. The lighting designer sets his stage lighting so that around theaction the lighting is relatively uniform (e.g. 3:1 contrast ratio), andmaybe also lights up some corners of the scene to avoid “black holes” inthe final rendering. Now having a display 110 with a maximum achievablepeak_white (this is especially true with e.g. LCDs with some fixed TLbacklights, but also with a CRT in which the beam strength could becontrolled via a contrast setting, for any setting there is still amaximum achievable brightness), it doesn't mean that this exactly has tocorrespond to the white of say a highly reflecting paper in the scene'ssweet spot. Because of the always existing variation in illumination,especially for the moving action in the video that would be impractical,and when one moves the paper into a zone of somewhat higher illuminance,quickly an undesirable clipping might occur. So one needs a little bitof safeguarding on the bright side, although for typical LDR scenes andprograms, that need not be very much. On the dark side one simply letsthe signal disappear in the noise of the camera. So on the bright sideone will render the white at a position W below peak_white PW. Not toomuch preferably, so that it still looks white and not light grey(there's some latitude on that for natural pictures comprised ofobject). Also the middle grey MG, and the colors of human faces whichfall around that, will then be reasonably visible, since they will bereasonably bright in the rendering. So that is the minimum requirementof any capturing or grading, that we can nicely see the acting of theactors in their faces (and eyes which may be somewhat shadowy due totheir protruded location in the sockets), and more specifically, theface colors of all those beautiful actors will look appealing (and nottoo muddy or pale). The rest of the (e.g. darker) colors then becomereasonable along the curve automatically. One typically uses a curvewhich is about a square root, historically because of the behavior ofthe CRT (electron gun non-linearity modulated by face plate illuminationetc.), and still uses that very useful curve because it happens to modelthe lightness characterization of human vision (i.e. luma values areapproximately lightnesses, the former being the mathematical encodedvalue in e.g. a Rec. 709 space, and the latter the psychovisualappearance for a human). Now the television can do some simpletransformations on that curve, e.g. it can boost all values by amultiplicative factor. Such operations, e.g. to counter for a change inviewing environment, have an impact on the psychovisual image appearanceproperties like the image contrast, and the contrasts of its objects.The camera can do similar operations. Now the question is where toposition the luminances like MG optimally, and how to easily do that. Ina simple system, an encoder 101 in the camera can transform all valuesbetween the Max_Luminance of the raw signal and whatever lies beneath,by bending them with a square root function defined from thatMax_Luminance. Then all possible camera-captured values from the scenewill be encoded in an image Im_LDR (here shown on a coded imagecomprising medium 102, like a Blu-ray disk, but it could also be asignal over a cable or the airways) so generated (typically onequantizes the values to e.g. 8 bit, and may perform other operationslike image encoding operations like approximation with discrete cosinetransform DCT decompositions). By squaring the values of the codedlumas, a decoder 103 can retrieve with the display the originalluminances of the scene as captured again as display-renderedluminances. Now in this tightly controlled system, there is a degree offreedom to accommodate for the minimum variations in a typical LDRscene. Using this square root allocation blindly, it may happen (if onedetermines the maximum on the scene on highly lit objects, bydetermining the exposure for those objects to have them stillwell-captured) that the middle gray and face colors fall too dark onsuch a curve. If there are many bright objects which should bereasonably well-captured, one would like a code-defining curve whichgoes down somewhat more slowly starting from the brightest RAW code.This can be done by offering the camera man a controllable knee point.He may e.g. select with the knee the input luminance level correspondingto his action sweet spot white, and put that e.g. on 90% of maximum lumacode (corresponding to peak_white on the display). He then has 10% ofcodes remaining for encoding all values above that, and he can adjustthe slope of the part of the curve above the knee, to incorporate e.g.luminances up to maximally 600% of sweet spot white luminance. In thisway he can tune his curve corresponding to the more plainly or contrastyan LDR scene is lit. If it's a low contrast scene he can put his kneepoint near maximum luma, and hardly encode any luminances above sweetspot white, and if he desires to have a lot of high luminanceinformation in, e.g. in a talk show where they talk about shiny metalsilverwork, he may incorporate some of the bright shiny highlights inthe coded luma signal. This simple system so automatically adapts to thebest grading of a particular LDR scene on the display side, i.e. itmakes a little extra room for the silverworks highlights by darkeningthe darker colors somewhat, and pushes some visible structure into thebrighter objects (majorly deformed compared to the original sceneluminances for those bright objects, and often with pastel colors due tothe gamut shape of RGB systems, but existing to some degreenonetheless). However, such an LDR system quickly clips for higherbrightnesses, and is not suitable to encode e.g. the outside worldvisible through the studio windows, which world is not cared about.Sometimes this leads to strange situations when a field working cameraman decides to photograph a person at the end of his living room whereit is relatively dark. Then half of the captured image showing thelighter parts of the room will be clipped to white when well-exposingfor the face. Where that may still be a minor nuisance on an LDR screenwhere the whites just show as some “white colored objects” but notreally luminous regions, this leads to quite a weird situation on a 5000nit display where half of the picture is glowing extremely brightly.

So the LDR system, its philosophy but also the capabilities inherentfrom its technical construction, is not suitable for HDR capturing,where at the same time one wants to capturing a first illuminated partof a scene, and a second much (e.g. 100 times) more brightly illuminatedpart of a scene, and maybe simultaneously even a very dark part, etc.

With FIG. 2 we now elucidate some of the principles behind variants ofour invention, namely a particular embodiment of an image encoder 202incorporated in a grading system. Shown is our automatic grading unit203, as being part of a grading system. Such a system may e.g. be acomputer running grading software, but it could also be a less complexsystem in which a human e.g. only at times modifies some settings of acolor mapping from a remote location. A human grader can specify hisdesired color mappings via a user interface system 230, which may e.g.comprise a dedicated grading console with trackballs etc. Coupled withthe software, he can so increase e.g. the color saturation of a selectedimage region, or drag a marked point on a tone mapping curve (e.g. redcomponent in versus resultant red component out for all pixels) upwards.We will focus our description on what happens on typically an alreadyoptimally pregraded master HDR image (e.g. received via an input 240connectable to e.g. a data server, or internet connection, etc.),although that might as well come straight from a camera, which may e.g.internally have done some grading. With FIG. 7 we describe an exemplarysystem of how one can define such master HDR gradings or images. Anycamera-capturing or grading is in fact just a representation of a worldscene, which needs to stimulate a human to get a reasonable impressionof that world scene, so it needn't necessarily be an exactly accuraterepresentation. In fact, one always needs to cross the difficultboundary from scene-referred, in which the camera just acts as a linearmeasurement device, to display-referred, in which a display needs toemulate to a human the original scene in a very different setting(although some of that complexity need not be handled in the masterencoding, but can be deferred to the display transforms). One candiscuss whether a master encoding should be able to accurately encodee.g. the sun, where a display will never accurately render the sun(which even when possible and sensible power-consumption-wise, would bevery irritating to the viewer on smaller screens). So why not allocateit to e.g. a fixed high luminance code (e.g. a pseudo-sun of 20000 nitinstead of 1 billion nit). Furthermore, a problem with scene-referredsystems that are able to encode all kinds of values that are not easy tomake visible, is that it is not easy to work with these color spaces.E.g. if a grader was to adjust the saturations of some saturated flowerswhich he cannot perceive on his current grading display, he may bemaking ugly colors for whenever that image is shown on a better displaywhich can show those colors. Perhaps that is a later reparableoperation, but one could ask why one does it in the first place, atleast on those colors. In the HDR encoding embodiment of FIG. 7, we givea lot of value to a large range of luminances, which can be reasonablyaccurately encoded (/graded) on a range of luminances of a high qualityHDR reference display, e.g. of peak_white 10000 nit (corresponding to aluminance value MAX_REF in a full HDR representation HDR_FREP). The ideais that one could have at least the most interesting colorscharacterized in such a high dynamic range specification, and the gradercan actually see them, and optimally position the luminances of variousscene objects compared to each other (e.g. darken the clouds). Thisrange of e.g. 0.01 nit (which we may simply call 0) to 10000 nit ofdisplayable colors will be our master grade M_HDR*, since we canoptimally grade it. The idea is that any display of lesser dynamic rangecan derive its to be rendered colors starting from the specification ofthe colors within M_HDR* (typically we will extract from the full HDRimage representation HDR_FREP, this range as input M_HDR for our systemof e.g. FIG. 2). This specification will probably also work reasonablywell for displays with higher dynamic range. E.g. the grader mayapproximately grade some bright lights of the scene, so that they willat least show bright on any display rendering. He may encode in thereference grade M_HDR* for the HDR reference display the brightest lightat say 99% (linear) of MAX_REF, and he may encode another bright lightto be still bright but contrastingly definitely less bright at say 80%of MAX_REF. An actual 20000 nit display may use simple scaling on thecodes of those lights, e.g. boosting both with a factor 2, which merelycorresponds to similarly (percentual) referencing them to its higherpeak_white. In this case both lights may be somewhat brighter—as theycould have been in the original scene—but largely the look of that HDRrendering is still similar to the reference look on the 10000 nitreference monitor of the grader. Actually if one wants to moreaccurately encode values higher than what can approximately be encodedin the M_HDR* (e.g. suitably darkening a light so that it falls withinrange, yet with a similar look, i.e. the exact scene luminance of thatlight being irrelevant) one may do so in the color space definition ofFIG. 7, irrespective of whether one would like to use those values laterfor a higher dynamic range display to more accurately render thosebright colors than with a stretch of the values encoded in M_HDR*. Soone can still add some really bright (or really dark) colors outside therange M_HDR* of what would typically give a really good rendering of anHDR scene, and one may typically strongly compress those colors, i.e.represent them with only a couple of values, with the codes in HDR_FREPbeing highly non-linearly related to the actual scene luminances. E.g.,when looking at a very high contrast scene like e.g. a welding scene atnight, there may be a long time nothing above the range M_HDR* encodingthe useful objects, and then there are the luminances of the arc. We mayrepresent those with a strongly posterized shape of the arc (i.e. a fewcodes) and put them right above MAX_REF. This would already be one wayto have a reasonable representations of that arc (already gamut mappedto colors which are near to those which are typically renderable on aHDR display), but if one wants one can also shift in metadata a functionof how to shift them to luminances more close to the actual luminancesin the scene (e.g. a luminance offset constant). We show a range ofcapturable luminances CAM_1 of a HDR camera which can do this capturing,and one part of it is encoded within M_HDR*, e.g. directly by (possiblywith a linear contrast multiplier) allocating the relative luminancevalues to luminance values within M_HDR*, or one can already use somemapping function doing automatically a first kind of grading (e.g.pushing the brighter or darker luminances somewhat more together). Thebrightest luminances captured by the camera are then stored in theoverflow range RW_STR_HI up to the maximum MAX_REP of the color coding.We have shown an example where we encode a certain level of darks ontothe code 0, and can with some mapping store even darker luminances innegative values up to MIN_REP. We also show how e.g. special effectspeople can draw into the color coding HDR computer graphics CG, likebright explosions.

Returning to FIG. 2, the human grader uses the image grading unit 201which is arranged to do any of a set of color transformations. This maybe a limited set of color transformation functions followingmathematical criteria like e.g. reversibility (with reversibility wenormally mean that in a sufficiently precise color encoding, like withfloats, one can reverse the function to re-derive an input image fromits output image after applying the transformation; with color we meanat least a luminance-correlate of a pixel, region, or object colorspecification), or preferably it's a broad set of functions which allowthe grader to grade the image in any manner he likes. Examples oftypical functions are those supported by e.g. Da Vinci Resolve, or AdobePhotoshop. Internally at this stage we may assume that all processingstill happens in [0.0, 1.0] float encodings of the color coefficients,with the precise definition including quantization coming in at a laterstage in the image grading unit 201. However the output LDR image Im_LDRwill typically already be encoded according to the image or videoencoding standard, e.g. for non-compressed signals it may be quantizedin an YCrCb color space, or it may be wavelet compressed, etc. Theactual formatting of this image, e.g. the chopping into data blocks,whilst inserting headers and other metadata will typically be handled bya formatter 220, which outputs an image signal TSIG, e.g. towards amemory for images 102. This signal may be stored on that memoryaccording to e.g. the blu-ray disk specifications, or according to somedefinition for storing on a flash card, or hard disk etc. The skilledperson will understand that similarly the image signal TSIG can be sentover some data connection, e.g. wirelessly to a home server withpermanent or temporary memory for storing the TSIG or the image(s).

In FIG. 8 we give an example of how a grader can grade starting from aninput image to create an output image. We will focus on the relationshipof the brightnesses of subranges, and later on give a few examples ofhow to handle the chromatic components of the pixel colors. Assume thatthe input (In) HDR image pixels are encoded with their luminances L_HDR,and the output values in LDR are encodings so we call them lumas Y_LDR.Although our methods are by no means limited to particular bit depths,assume the lumas range in [0,255]. Now the grader will study theparticular input image to process (for video this will be a key image ina shot of images to be color mapped similarly), and design an optimalcolor mapping, in the example a multisegment mapping according to hispreference. Suppose we have a covered part (e.g. under the shade oftrees, where the main actor resides) below HDR luminance Lt_1, and somehouses in the background which are brighter. So their HDR pixelluminances will fall above Lt_1, but it is not necessary that thedarkest one falls directly above Lt_1. Furthermore there may be a verybright lamp with luminances above Lt_3. Now this scene is different fromour above classical LDR scene. We have two interesting scenes, the shadearound the actor (in which we desire to make the actor well-visible, yetclearly darker than most of the scene) and the sun-lit houses in thesurrounding background. The grader can e.g. elect to make the regionbetween Lt_11 and Lt_12 which contains the face colors sufficientlybright and contrasty, to make the face clearly visible. He may do so atthe expense of darker colors, which need to be encoded with few Y_LDRcode values, and below Lt_13 they will even be clipped to Y_LDR=0. Hewill also encode/grade with less contrast/precision the luminancesbetween Lt_12 and Lt_1 by lowering the slope of that segment. Thiscreates room in the Y_LDR range for the sunlit houses, which he writesin with curve defined by dragging a control point CP. The lamp colorsafter a luminance discontinuity of non-occurring colors, can be encodedright above the end of the houses luma Y_H, or starting a couple ofcodes (e.g. 10) above that.

Now in our LDR-container philosophy this color mapping curve can bothfunction as a color look-optimization curve for the output picture, i.e.e.g. typically an LDR grading derived from an HDR grading, but also as acode-defining curve. Analogous to the gamma 0.45 curves of e.g. MPEG,which define a luma coding for each input or rendered luminance, ouroptimal curve defines the allocation of particular code values to thevarious luminances in the input HDR image. But hence simultaneously thevarious image objects or their luminance subranges also are alreadycorrectly positioned along the luma axis for directly (or perhaps withminor transformation, which may involve physical display characteristicprecorrection like EOTF handling, or minor tuning towards a particulare.g. darkened viewing environment via a simple display transform)driving of an LDR display.

So we have changed or generalized a couple of fundamental truths of theLDR encoding technology. In particular, asking the question of what iswhite in HDR (the white paper in the sweet spot illumination indoorswhere the action occurs, or the white paint of the sunlit housesoutside; with the human vision also capable of very cleverly handlingall those semantically), we have abandoned the vision of tyingeverything to a particular white, or a correlate thereof like “the”middle grey of the scene/image. As an alternative thereto we come upwith color regimes, which can function on themselves, whatever theluminance relationship with particular colors like some white. E.g.there can be the regime for the sunlit houses or the lamp, which canhave their own handling, which now without necessarily accuratelyreferencing can be semantic-relational rather than precise numerical. Wehave already given the example of a bright outdoors, or a very brightlight. Instead of with a fixed luminance relationship making the (e.g.average, or lowest) outdoor brightness e.g. 5× brighter, we can makethem just “a fair amount brighter”. The fair amount can then bedetermined ultimately at the display side. E.g. a very bright HDRdisplay of 15000 nit may make the outside 20× brighter, but a limitedone may need to cram all sunlit houses colors in an upper range which isjust on average 1.5× brighter than the indoors colors, just giving aninitial simulation of the fact that it is sunny outside. Similarly, adark region can be rendered not as exactly with luminances Lx, Ly, etc.,but as “barely discriminable”. Smart HDR decoding and optimizationsystems can take the specifics of the display and environment intoaccount, and can further optimize starting from the defined grading tunethe final driving signals.

So secondly this means that we abandon the concept of a single fixedcode defining curve like a master overall gamma 0.45 which being closeto human vision is considered to be suitable at least over the entireLDR range of luminances. Whether we use any color grading as strict(i.e. the display should try to render it as close as possible as itwould look on e.g. an LDR reference monitor, i.e. with minimal owntuning) or as liberal (where the grading is just a relative guidance,stating approximately how one can deform the colors to keep somewhat ofthe artistic intent in e.g. a more limited physical range ofluminances), we will allow the grader to create in some embodiments evenarbitrary code definition functions, which may even be discontinuousfunctions.

And thirdly, we state that there should no longer be a single gradedimage, but rather the gradings need to be optimized for each renderingsituation. And human vision being complex, especially the more renderingsystems vary in their properties, the less correctly this can be donewith (especially simple) automatic color mappings, and the more severalgradings should be optimally made by human gradings. But in practice wesee that for many scenarios given necessary investments, it will besufficient to have only two gradings (a classical for LDR, and an HDRgrading for the HDR systems), and where more precise tuning is needed,the systems can then closer approximate good gradings by technicallyinterpolating or extrapolating them on the basis of these two gradingsfrom their comprised artistic information.

Stopping after the grading of Im_LDR, and writing that LDR containerimage onto an image memory together with a prediction function forreversibly reconstructing an approximation of the master HDR M_HDR fromit (perhaps for some systems a grader using strictly reversible colormappings is not necessary, since for lesser quality systems it may beenough to reconstruct a REC_HDR with significant deviations from M_HDR,as long as the HDR effects derived from the Im_LDR data still produce areasonably similar HDR look, in which case one may co-encode such HDRreconstruction color mapping functions, which have inverse which thenare approximations of the actual color mappings used by the humangrader), would be good for systems with e.g. 12 or 14 (non-linear) bitsdefining the Im_LDR encoding (depending on the requirements of thevarious applications). When going to tighter specifications, like e.g. 8or 10 bits (of which most people would say they are difficult to encodeHDR images, but since humans only discriminate only a couple of millioncolors, and depending on the application like a fast moving video withconsiderable noise the amount of needed colors may be even less, so ifone were to encode the required most important colors into the 8 bitcorrectly that should be possible), it may be useful to do the furthersteps of our invention to guarantee an improved quality of both the LDRand HDR grading, whilst allowing the grader maximal flexibility as tohow those gradings should look (i.e. in which luminance (-correlate)subranges all objects should fall).

We now assume in the continuation of our FIG. 2 example that the gradergrades very liberal with an arbitrary luminance-mapping function, andthe LDR lumas are quantized to only 8 bit. The slope of the mappingcurve between Lt12 and Lt_1 may be so low, that there are too few codesto faithfully represent those colors, e.g. of vegetation in the shadow.In an LDR rendering that may not be so objectionable (indeed it isn't orthe grader wouldn't have specified the curve in that way), however, uponreconstructing the HDR luminances for these objects, the posterizationmay give an objectionably low texture quality to those objects in such ahigh quality rendering.

Therefore, the automatic grading unit 203 is going to analyze the LDRgrading Im_LDR of the human grader, and identify and solve such issues.There are several ways the unit 203 can do so. It can e.g. purely lookat the images themselves, and compare spatial regions thereof. E.g. itcan look at a region in Im_LDR (such as a dark face) and count thenumber of luma codes representing it. The internal rules of the unit mayspecify that any region, or especially a face region, should not berepresented with less than MB (e.g. 10) different code values (or ingeneral a number of colors, typically based on the number of lumasthereof, but similar restrictions may be counted on the number ofrepresentable different saturations e.g.). Or the unit 203 can comparethe amount of codes in the region in Im_LDR with the amount of differentcodes (typically luminances, but M_HDR could be encoded with differentluminance-correlates too) in the HDR image. If there are many differentvalues in M_HDR, there should be a reasonable number of lumas in Im_LDRfor that region too. E.g. the rule can be that the fraction of theIm_LDR lumas versus the HDR luminances should not be less than ⅕^(th),or 1/10^(th) etc. One can similarly define relationships based on rangesin floating point luminance space for M_HDR. More complex image analysiscan be done, e.g. studying geometrical shapes and estimating how suchshapes deviate when represented by less colors. E.g. a detector canidentify blobs in shapes. This is a generalization of a bandingdetector, which checks whether there are runs of a number of pixelshaving the same posterized value in Im_LDR or in fact REC_HDR, wherethey do not exist, and are in fact smooth functional transitions inM_HDR. Further information can be obtained from texture estimators,which may determine e.g. the local complexity of image regions, etc.Even if the determination of the automatic grading GT_IDR is not goingto be based solely on the analysis of any of the obtainable images(gradings) themselves, it is useful if the automatic grading unit 203comprises an image analysis unit 213 capable of doing any of the aboveanalyses, since the resultant data is useful even when specifying atemplate curve, in a system which works on the color mapping curves ofthe gradings.

Thereto a curve determination unit 211 is comprised, and we willelucidate some of the possible embodiments thereof with FIGS. 9 and 10.In any case, whether the determination of the automatic grading isperformed based upon analyzing images, mapping algorithms like luminancemapping functions or both or any other analysis or prescription,typically the curve analysis unit 211 will have a unit determining andoutputting a final color mapping algorithm or function Fi(MP_T) (andpossibly also a unit performing one or more of several algorithms ofcolor mapping curve or algorithm analysis, whereby the algorithm can beanalyzed in itself, or as how it behaves on colors when represented bycurve(s)). This automatic mapping is now how one can derive GT_IDR fromM_HDR, so a color mapping unit 215 will derive GT_IDR by applying themapping Fi(MP_T) to M_HDR. Of course one needs to take into account inthis new formulation what the human grading Im_LDR was, now that onewill reference everything to GT_IDR. So an LDR mapping unit 217 willanalyze how the Im_LDR can be obtained from GT_IDR, and derive theparameters therefrom. If a pixel luminance maps from L_HDR=2000 toY_Im_LDR=180, and to Y_GT_IDR=200, then one can derive a mapping betweenthe latter. In such a functional form, the Y_Im_LDR values along therange can be derived by applying a per luma function which multipliesY_GT_IDR by (Y_Im_LDR/Y_GT_IDR). Similar strategies can be derived forother mappings.

With FIG. 9 we elucidate a computationally simple way to redetermine thehuman grading into a technical grading by studying the human grader'sgrading curve (whether solely, or aided by image analysis, orpotentially even by human grader interaction). We will look at a curvedeformation example, but the skilled person can understand that asimilar analysis can be used to select one of a set of well-functioningcurves (one or more CRV_i curves in FIG. 2). If the grader desires asmooth contrast-less behavior in a middle region, and stretched behaviorin outer regions (e.g. on the HDR the predominant regions where the mainaction occurs may need to be silky smooth, but lamps in the backgroundmay be rendered more coarsely, even banding may not be noticeable or atleast important, at least in some situations), one can select (basede.g. on a calculation of a functional correlation) one out of a set ofpre-agreed curves which matches best such a behavior, but does not havetoo high a quantization for the middle part. Such a selection may beguided by further image analysis, like determining which class an imagebelongs to (sunny outdoors, or nightscape with some bright lights),looking at the histogram distribution and its parameters (e.g. locationsand sizes of estimated lobes, etc., either (semi)automatically, or bythe experience of the human color grader). I.e. there may be a couple ofpredefined curves 901, which give reasonable behavior, at least from aprecision point of view. An actual image has to be coordinated aroundsuch behavior, taking the grading look wishes of the grader intoaccount. Of course whether an actual quantization is good versus atheoretical optimal or reasonably working situation is also dependent onhow many pixel of particular color there are in a particular image. E.g.if the dark part is just a small patch of looking through a grid into asewer, say 50×50 pixels in the bottom-right angle of an image, then somequantization may be quite allowable for the grader, at least for thatimage, shot or scene. I.e. the various curves may both function as afinal selection for the technical grading (in case there is one, or acouple master guiding curves—e.g. depending on target display whitepoint like whether the image is intended primarily for 1000 nit or 10000nit displays, or further characteristics of rendering environment orimage properties—determining quantization precision over the luminanceor luma range), or they may function as starting points from which thetechnical grading curve may be finetuned, until it maximally stretchesits code precision deformations for the more critical image gradings(typically the to be reconstructed HDR), and from thereon we can encodethe further requirements on the other grading look (typically an LDR)merely by transformation functions to be applied to that technicalgrading GT_IDR.

But now we will describe an exemplary curve deformation algorithm. Wewill calculate a specific embodiment of an amount of information, whichis an amount of used codes NC per luminance subrange (and we maydigitize a continuous range in M_HDR also by typically uniformlydistributing some integer codes along that range). We will look at aspecific test-range under study (between L3 and L4), but, although someembodiments may test only some ranges, like the dark ranges, it isadvantageous if all ranges of M_HDR luminance are so tested. One cansimilarly formulate whether some range of M_HDR is mapped to say 5 Y_LDRcodes, or whether a subrange thereof is mapped to a single Y_LDR value.

The method starts from a reference encoding function 901 (REF_CODF inFIG. 2), which specifies how many codes are needed for each range to bewell-reconstructable in REC_HDR. The skilled person should understandthis depends on, and can be calculated from technical parameters likethe dynamic range of the reference display belonging to M_HDR, theintended specifics of the Im_LDR grade etc. Such one or more referenceencoding functions, although they could be calculated in anyencoderon-the-fly (and may then be optionally outputted in the signalTSIG as metadata), may typically be precalculated in a design laboratoryof e.g. a grading software manufacturer and hence in an agreed waystored in a memory of at least the encoder (in principle the decoderdoes not need this information, but may also have it, e.g. in casefunctions like Fi(MP_T) are defined as relationships to such a referencefunction, but that will typically not be the case for simplicity). Theencoder may if it has several variants choose one, depending on how thefinal Im_LDR and REC_HDR are to be rendered, and this may happen withsome software selections of the human grader. Function 901 specifies howmany codes are needed for each interval of luminances. E.g. in theexample it was decided that only three codes of the 8 bit Y_LDR_min (theminimum number of required codes, given an allowed reconstruction orrepresentation error), i.e. in fact of the Im_LDR, will be used for allHDR luminances darker than L1. So these dark regions will be coarselyquantized, but they will have some structure nonetheless. If onebrightens these regions up severe posterization of the local imageobjects may occur (whether in an HDR reconstructed rendering, or amodified LDR rendering via a brightening display transform), but thismay have been a final decision to be able to encode enough HDR images inthis 8 bit code (if the set of codable images contains critical highcontrast multiple regimes images, one must typically at least sacrificesome quality). However, oftentimes the dark regions will be rendered sodark, that one cannot see too much detail in the display face platereflections of the viewing environment anyway. To be able to reasonablyfaithfully (given the sensitive human eye when the reconstructed REC_HDRis shown in a dark viewing environment) render the dark colors in theinterval up to L2, the curve prescribes that C2-3 luma codes at minimumare required (one may use more codes of course). Defining such a curvemeans that one may encode HDR images up to a maximal LDR luma Cmax equalto e.g. 255 (if the LDR container has 8 bits available for its luma;n.b. this can be simply seen as the gamut diamond up to 1.0 being fixed,and quantized with equidistant bins, but the distribution of the imagepixels varying dependent on the applied transformations, HDR images e.g.oftentimes having a large percentage of the pixels below 0.1), whichcorresponds, if this 8-bit container is actually encoding an HDR image,to a maximum luminance L_HDR of e.g. 10000 nit, depending on the curve.Note that the curve can be tuned to take into account the relativescaled nature of rendered luminances, and in this description we meanthat we can represent HDR reference display luminances between e.g.L1/10 (as a reasonable black still quantizable in the 0 luma) and 10000nit, but one may of course always adapt by scaling so that some otherluminances are represented. For simplicity one can safely assume thatboth L_HDR and Y_HDR have axes between 0.0 and 1.0, and then quantizedto some precision.

The skilled person will understand that this reference encoding function901 can be specified by several factors. E.g. when the human graderscribbles over a region of the (e.g. M_HDR) image, which may be e.g. aface region, the image analysis unit 213 may therefrom determine a rangeof HDR luminances wherein those face colors fall. It may then respecifythe curve so that more lumas are required to represent it. It may knowthat e.g. if the grader types a “face” indication button, how many codes(i.e. quantization bins) are typically needed for any situation (i.e.e.g. a face not well-illuminated according to the 36% specification, bute.g. falling in a darker shadow area of the scene making its averageluminance e.g. 10%, and the LDR container being e.g. 10 bit and arequired HDR rendering optimal for e.g. 4000-5000 nit). This would leadto a prescription of a number of bins (typically on the HDR axis, but itcould also be on the Y_LDR axis) around the point of average currentface color. This may be automatic (e.g. doubling or modifying the amountof JNDs for HDR reference rendering covered by this range), or thegrader may directly influence or specify the shape of function 901 inthat region. The curve can be specified or re-specified (if needed) onmany properties, like e.g. measurements of the HDR_image. Now looking atthe actual luminance mapping curve 902 of the grading producing Im_LDR(i.e. the grader currently taking nothing into account aboutquantization and data loss and the like, but just defining artisticallywhere he wants his objects colors to be in the normalized gamut for thisimage, to realize e.g. a dreamy look with may high brightnesses calledhigh key), we find that in the interval [L3,L4] the amount of actualoccurring codes given this mapping curve from the HDR master grading tothe chosen LDR look is smaller than the minimum required amount NC forthat region (we have shifted the curve for clear superimposition, but ofcourse the determination of used lumas can be simply done for anycurve). Note that we have assumed that the lumas are determinedequidistantly in the ranges, but one may take non-linearities intoaccount similarly, e.g. by focusing on (dis)allowable mappings to singlelumas. But normally on the Y_LDR axis we will have equidistant bins, sowe can discuss in that way without losing generality of teaching.

So we currently use too few codes in that interval (which could show asa low contrast in LDR rendering, but probably not as the grader has justoptimized this curve, but will typically show up as to coarsequantization for reconstructed HDR images), and need to stretch thelocal slope of curve 902. There are several ways to do this, e.g. withelastical error functions which penalize quantization errors over anyinterval. In general we could have any mathematics taking into accounton the one hand size of particular intervals, and on the other handaverage luminance/luma positions of those intervals, i.e. how much thecurve deviates from the intended grading. Of course, if the technicalgrading demands one needs a particular mapping curve shape which is faroff from the shape which the grader desired for the LDR look (i.e.technical HDR-data requirements or the code allocation purpose of themapping function being far off from the “LDR” requirements or therendering look purpose of the mapping), then the grader will continuewith defining his look in another, additional way, via further mappingfunctions. So in principle no tight specification or criticalmathematics is needed for this method, but of course some methods willbe less calculation-complex, or more user-friendly in how swiftly thegrader arrives at the desired encoding+grading result (as grader time isexpensive, at least for some kinds of program).

We elucidate a simple curve transformation with FIG. 10. If Nc1 is thecurrent amount of lumas allocated to the interval, and Nc2 is the neededamount of codes (whether minimally needed, or somewhat larger), one maystretch that part of the curve by e.g. multiplying around the midpointwith Nc2/Nc1. The rest of the curve has to be modified, and we assumethat redistributing the quantization to fit in the total amount of codeswill already give a satisfactory result. One can e.g. derive the part ofthe curve above L4, by taking that curve, offsetting it with OFF_1, andscaling it so that the maximum still falls on the maximum luma. By doingthis everywhere one obtains the technical grading curve 903, which isthe curve of color mapping Fi(MP_T). The lumas on the Y_LDR_min axiswill then form the GT_IDR image. The system may check whether there isanother range which then becomes critical, and then e.g. balance theerror between the two regions. E.g. ideally Nc2 should be 10 codes, andNc2* on another range of L_HDR (i.e. also Y_LDR) may need to be ideally8, but if there is only room for a total of 16 codes, one may distributethe minimal quantization error as 9 codes and 7 codes. Of course thiscan be weighed by such factors as range of the two regions, semanticsignificance (are there face colors), etc. If necessary, the system canprompt the grader to choose which of the two areas should be better,e.g. with a user interface allowing him to increase the number of codesNc2 (i.e. the local slope) in steps, which then means less codes forNc2*, which the grader may think visually acceptable. Of course someembodiments may work fully automatically behind the scene when selectingthe technical mapping curve and grading GT_IDR, and in that case thesystem may e.g. just abandon the finetuning around a preferred LDRgrading curve and immediately jump to one of the predefined well-workingtechnical curves (e.g. one that has a least deformation compared to theLDR look defining curve (902), like calculated asSUM(wi*[FT(L_HDR)−FL(L_HDR]), in which the brackets indicate somefunction like an absolute value or square, the FT is the currentlyselected technical function per value L_HDR, and FL is the preferred LDRlook mapping of the grading, and the weights wi may be uniform, but alsoweigh certain areas of L_HDR more, like e.g. where the faces reside), inwhich case the look is then defined by the further mapping functionFi(MP_DL). Error measures may also take slopes into account, since localslope identifies amount of available codes in the LDR representationversus required codes in the HDR region. Note that even changing oneinterval in fact distributes an error all over the range compared towhat the grader would like to see, but that needn't necessarily be alarge error, as it is distributed and seen relatively as the eye is notreally designed to function as an absolute luminance meter anyway, andin any case it can for most practical systems be calculated away againat the receiving side with Fi(MP_DL). The skilled person understandsthat there can be many other ways to realize similar functions. E.g., itmay be so that there are enough codes (because the M_HDR image doesn'tgo all the way to L_HDR_MAX corresponding with Y_LDR_MAX), and one mayhave a lot of freedom in respecifying at least some parts of function903, yet curve 902 was still too quantized in interval [L3,L4], and hasto be corrected. In such a scenario one may more freely shift themidpoint luma of the interval [L3,L4], and the other curve regions. Suchscenarios correspond to brightening of some objects etc. The otherscenario is where the system is really critical, and redistributing thequantization outside interval [L3,L4] may lead to inappropriatequantization there. In that case mitigation strategies can be used todetermine a final curve. One example of a mitigation strategy is todivide the remaining errors over the most critical ranges, like [L3,L4]and [Lx,Ly] outside where the biggest quantization error occurs for thecurrent grading curve, or any curve trying to keep a reasonably closeapproximation to the current human grader's curve. One may also decideto allocate the errors strongly to some regions. E.g. one may clipluminances to even somewhat above L1 to the single 0 luma value, or onemay decide to clip on the bright end, even in the technical gradingGT_IDR. The REC_HDR image is then not perfectly reconstructable, butsuch scenarios can be used in systems which have an overflow correction.E.g., the clipped values can be encoded in a second image, separate fromthe GT_IDR, which contains only the data for a bright, clipped region inGT_IDR. When comparing the actual mapping curve with one that has goodtechnical properties (characterized in that it has at least a minimalamount of codes per interval) of course the automatic grading unit willcheck whether there are actually any pixel colors in that interval,otherwise it may seriously distort the function in that range.

FIG. 12 (FIG. 12b ) shows an example of how a grader can influence thetechnical curve allocating the codes used for the technical gradingGT_IDR. As said all of this can in some embodiments happen behind thescreen without the grader knowing it, but here we give an example of howa grader can specify or influence the amount of codes allocated to aparticular region in the HDR luminance range 1210, let's assume whichcontains facial colors. Let's suppose that in this example the automaticallocation of code zones was pretty good (possibly taking into accountthat we have only 8 bit luma instead of 10 bit available, or perhaps for10 bit a less appropriate colors space which introduces too severequantization at least for some colors like e.g. saturated blue) but thegrader looking at his quality reference display wants still somewhatmore precision, e.g. to have a less blotchy face. He may then considerthe local slope in luminance range 1210 to be too low, and may want toincrease it via user interface slope changing means 1203, which may bee.g. an arrow which increases if one clicks the top arrow increases theslope with X %, or a draggable cursor, etc. He can specify range 1210directly in his curve tool viewing window and drag one or more limitsetter(s) 1202. The user interface may also aid quick selection, by e.g.allowing drawing a scribble 1215 over the currently gradedrepresentative picture from a scene (see FIG. 12a ).

All this time the grader is looking at the reconstructed HDR imagerendering. If he now wants to work on the LDR image again, he switchesto that viewing, and specifies his LDR grading further again startingfrom this technical curve, into an additional mapping curve or strategy.Motion tracking means for tracking the face and finetuning itsproperties if it walks under variable illumination may aid in thedetermination if necessary, but in general we will not need suchcomplexity for the present invention, as the technical curve is onlysupposed to be in general largely good, and not ultimately specific. Butin any case the grader can be offered finetuning at any moment of themovie he considers it interesting, of both the technical curve and themapping curve for obtaining the optimal LDR images. Now the software maybe configured to change the slope compared to the midpoint (curve 1204).However, the grader may consider this to introduce grading color issueswhich he may want to address now (rather than in the second LDR mappingfunctions). E.g., when the algorithm or hardware calculates the newcurve, it will in the simplest versions reallocate the error, which itmay do e.g. by stretching the remaining shape of the curve to themaximum 1.0 value, starting from the new high-point of the locallystretched interval. But the grader may consider this to give too brightcolors in regions 1205. Therefore the software may have positionadjustment means 1206, which allow the grader to shift the local curvein range 1210 upwards or downwards somewhat, yielding the finalreasonable curve 1207. The grader may also specify in similar mannerregions where he considers the quantization errors may be more severe,e.g. in this case slider 1201 may allow him to set a lower boundary fora range of bright colors which may be quantized somewhat more whenneeded. If one needs to balance color properties given all technicallimitations, this may be a good way to arrive at a reasonable optimum,especially if the original material was not captured perfectly in thebrights anyway, but e.g. with somewhat pastellized colors. This slidersthen e.g. gives the reference HDR luminance position above which thereare e.g. 20 m codes, distributed via e.g. a gamma 2.2, or psychovisualJND-based curve, etc. In this case the algorithm mathematics can takethis into account when redistributing the errors, e.g. by penalizing adifference from the 20 codes in a weighed way with the codes remainingbetween the high point of range 1201 and that low value of the upperrange set by 1201. Of course the grader if he considers the issuecritical enough to spend more time may also select one or more of suchranges to finetune, and e.g. add a fixation resistance to the alreadydetermined curves, indicating that they may not lose any codes ormaximally 20% codes, or lose codes at a rate 10× lower than the currentinterval etc. This provides some inertia in respecifying anotherinterval. But usually the grader will not have to recode many criticalregions, otherwise he may just let the hardware come with an automaticproposal.

FIG. 3 shows a possible embodiment of an encoding system following theprinciples of our invention, where the human derives his grading from atechnical grading GT_IDR. The reader will understand that technicalvariants we teach here (e.g. regarding suitable technical color mappingsetc.) will also be applicable to the FIG. 2 class of embodiments orother embodiments, and vice versa.

A color mapping derivation unit 214 determines a suitable color mapping(e.g. luminance mapping curve, and corresponding chromatic colorcoordinates handling strategy) to map M_HDR into GT_IDR. The mainpurpose of this is to determine a grading GT_IDR which is most suitablefrom a technical point of view. In particular, one should be able toreconstruct a REC_HDR (by applying CMAP_2 which is the inverse colormapping of Fi(MP_T)) which will be a close approximation to M_HDR(according to some image deviation criterion), or at least fall within apredefined second accuracy from M_HDR. The skilled person understandsthat there are defined several ways to measure deviations betweenimages. E.g., a popular measure is PSNR, but that is a rather simple,blind measure, which sometimes can give high contributions todifferences in noise which are psychovisually hardly visible whereasthey measure some real object deviations to a lesser extent. So we'dlike to use measures which more tightly measure what happens to thevarious object, especially in a mathematical framework correlating withpsychovisual principles. E.g., the image analysis unit 213 can do someapproximate segmentation of the REC_HDR and M_HDR in segments(pseudo-objects). It can e.g. look for relatively smooth segments, andmeasure an amount of posterization there. A quantity can be e.g. theamount of colors used versus the area of the smooth gradient region,which will result in an accuracy measure which is similar to countingrun lengths of runs of pixels with a same quantized color. One may alsocalculate functional correlations or accumulated differences between theoriginal M_HDR luminance shape over space, and the staircased functionin REC_HDR. The skilled person will understand that one can introduce(pre-)semantic information in our evaluation of the accuracy and theresulting choice(s) of mapping algorithms. E.g. if there is a smallobject only, especially if it is in the background near the side of theimage, the object is probably less important and we can encode it withless luma codes, making more codes available for other codes. Theskilled person will understand that a total accuracy or error (e.g. asan accuracy image) can be formed from a pre-agreed (e.g. loaded in theautomatic grading unit 303 via a software update) set of measurementalgorithms, which can take into account geometrical properties like sizeor position of a segment or object, statistical properties like whatkind of texture or color the segment/object is, semantic properties likewhether we are looking at a face or sky (with a face or sky detector),etc. The accuracy mathematics may also have special measures foranalyzing the HDR effects, e.g. an explosion may be characterized not asan absolute difference of the pixel colors between REC_HDR and M_HDR,but with a relative measure which is based on such parameters like adifference between the average color in the fireball and thesurrounding, a variation of colors in the fireball etc. The REC_HDR willthen be seen as sufficiently accurate if a measure thereof is below orabove a threshold, i.e. even if the fireball is a little less bright orcontrasty in the reconstruction, as long as it has sufficient impactbecause it is still much brighter than the surrounding, thereconstruction is seen as a good HDR reconstruction. Such variants areespecially useful for systems which are more critical due to tightnessof physical resources like the amount of bits in the GT_IDR encoding.HDR effects may be characterized in a complex way, or just selected asregions of high brightness, e.g. above a relative threshold LT. Theskilled person will also understand that in e.g. a recursive strategy tocome in steps to the optimal color mapping Fi(MP_T), the color mappingderivation unit 214 may not simply determine its mapping based on anaggregated total accuracy, but finetune based on the partial accuracies.Similarly to our local adjustment example of FIGS. 9 and 10, the unit214 may cure a face which is represented to coarsely, because itidentifies patches in the face, and then allocate more codes by changingthat part of the function. The accuracy criterion need not be met bycalculating it perse. Rather, we can use a set of pre-agreed functionsor color mapping algorithms ALG(CRV_i), which are considered toreasonably fulfill the accuracy criterion for a particular applicationfrom a practical point of view. Even if a selected optimal mapping curvestill introduces a somewhat more severe error in some part of someinconvenient M_HDR image, that is then considered as acceptable. Thedetermination in any of those scenarios can be both automatic inside theunit 303 without any manual intervention bothering the artistic grader,or it can be partially guided or fully determined by the grader, e.g. byletting this grader select one out of a number of possible mappingalgorithms or curves. Typically the unit 303 will know, and the graderwill have set, some generic parameters regarding the mapping andencoding situation, e.g. the dynamic range (e.g. CODR or CDR) of theM_HDR image, and the grader may have selected from a menu list that heis currently grading a “night image”, etc.

E.g., the image analysis unit 213 may look at the M_HDR image, and findthat there are two well-separated lobes in the luminance histogram. A(at least initial) mapping function may then be derived which maps thoseto appropriate subranges of the 8-bit luma code, taking into accountthat the human visual system is more sensitive for the darker parts,which will hence need a larger subrange. So on a coarse scale themapping could go to e.g. [0, 170] and [180, 255], i.e. any functionwhich realizes such is a viable candidate. Within these ranges furtherbending of the mapping curves of Fi(MP_T) may be done, e.g. giving facesa somewhat higher number of codes (which to be clear need not persecorrespond to a higher contrast in the face in the ultimate rendering,since the display transform can still reduce the contrast over the face,but then at least we have a good precision of the facial texture andillumination).

The simplest versions can be e.g. a set of parametric gamma-like curvesY_LDR=k*L_HDR below L1 and 1*power(L_HDR, gamma)+off above L1. In thiscase the automatic technical mapping determining algorithms maytypically evaluate what the image structure is in the dark regions, anddetermine a sufficiently well-characterizing linear part therewith. Ifthere are many objects, especially with a complicated geometricalstructure (like a shed containing a lot of objects, like woaden boardsstored for later construction, metal frameworks, tools, etc. all stackedin between each other in the dark), then the unit/algorithm may decideto allocate more codes to this, by adjusting the first (e.g. linear)part of the gamma-like curve. Similarly, if there are actors in thedark, the system may want to characterize them with sufficient codes,even if they are ultimately rendered very darkly, and the viewer wouldnot see too much detail in the bodies anyway (but note that a viewercould always via his remote control apply a brightening displaytransform, and a good encoding should cater to that).

Similarly the image M_HDR may be analyzed and segmented in a middle part(e.g. further image analysis algorithms like a motion analyzer can helpin determining a region of main action), a bright part, and a dark part,and then a sigmoidal or three-segment curve can be determined for that,etc.

Alternative, the human grader can be prompted to select an optimaltechnical curve via his user interface 230. E.g., he may choose theoptimal one from a number of gamma-like curves, but the skilled personunderstands this could be other fixed pre-agreed curves, and in arecursive technical optimization the grader could even start tuningcurves by e.g. dragging control points CP. The curves may then e.g. havesome internal elasticity mechanism, forbidding the grader to choosesegments with too low a slope, or other inappropriate characteristicslike inversions, double allocation (which cannot be reversed as aCMAP_2), etc. Typically the algorithm will come by itself to a steadystate (initial, or momentary) e.g. by sounding a warning if the curvebecomes unusably deformed, and then resetting it to a similar one withgood reconstruction properties. Typically the system will then generatethe REC_HDR, and allow the grader to toggle with M_HDR on his referenceHDR display, to see the accuracy or errors. The system will send theGT_IDR to a reference LDR display, so the grader can also check that.This image may already be sufficient in some scenarios and then thegrader need no longer make an second Im_LDR, but even if it is a lesserquality LDR grading, some receiving systems me still want or need to useit (e.g. because of a backwards compatible implementation in the TSIG,for a legacy BD player which ignores the color mapping data and justplays GT_IDR; but also e.g. GT_IDR may contain useful information fortuning/interpolating to a final to be used grading for a medium dynamicrange display etc.). In case the grader is satisfied, he will continuewith the next image or shot of images, and otherwise he will change someof the curve or algorithm further. The encoder may have means to helphim with that. E.g. when he scribbles inside a region with largererrors, the encoder may draw on the curve which luminance interval thesecolors fall in. There may even already be initial analyses of theartefacts, and suggestions (e.g. “doubling of the amount of codes issuggested” and already applying the new curve to obtain a secondREC_HDR_2 (by color mapping unit 215), since the unit/algorithms must doas much themselves to spare a busy artist as much as possible from thistechnical grade—although it is important since it is an easy roughgrading for the final look).

Now in the FIG. 3 class of embodiments, the grader will continue tofinetune on the GT_IDR image by using color grading unit 301, to obtainhis optimal look as grading Im_LDR. In principle he could applyliberally now any color transformation, since IM_LDR is not used toreconstruct REC_HDR. However in a practical system it is useful if alimited set of color mapping algorithms is supported, which allow mostor all of the color changes a grader may typically want to do, sincetheir defining data Fi(MP_DL) needs to be encoded in the signal TSIG.Some signal standards could be designed preferably upgradable, so thatdata of later new color mappings can be written in the metadata (with anew type indicator, ignorable by older systems). This is useful forfuture decoders which are easily upgradable, such as software running ona computer for decoding movies purchased from an internet-moviedatabase. For systems with a shorter turn-over, for which only atcertain times (expensive relative to the total system cost) a newprocessing chip will be designed, it is better to a priori agree on afixed set of color mappings (e.g. functions realized as LUTs etc.).Finally all data GT_IDR, Fi(MP_T) and Fi(MP_DL) (or derivations thereof)is formatted by formatter 220 to the specifics of one or more electedsignal formats, and sent outwards over some signal communications means.

FIG. 4 shows one possible embodiment of a receiving system, and theskilled person will understand that the can be many such systems. E.g.the image decoder 401 can be comprised in a separate unit (like a BDplayer, or STB), it may be comprised in a display or display-comprisingapparatus itself (e.g. a television, or mobile phone (n.b. the mobilephone although it may not have a HDR display, may still need to read theHDR encoding, and extract the Im_LDR grading therefrom)), a computer,etc. There may be professional systems which comprise the encoder too,e.g. a transcoder on the premises of a content provider, which e.g.creates from a first variant of HDR encoding according to the presentprinciples an image encoding in a second variant, to be distributed e.g.along a pay-per-view system, etc.

The image decoder 401 comprises an image derivation unit 403 which isarranged to do the construction of all the required images. E.g. it mayextract the color mapping data Fi(MP_DL) and do an MPEG_HEVC decoding onGT_IDR. And then it applies the color mapping to derive REC_LDR. We havealso in this embodiment a system configuration unit 402, which may bearranged to e.g. check what kinds of displays are currently connected,or what kind of storage devices need particular forms of reconstructedimages (e.g. a REC_HDR, or an interpolated grading REC_MDR, etc.), andit may suitably control the image derivation unit 403 to do the requiredprocessing. In this example we can send data (whether an alreadyoptimized e.g. HDR image for the connected display, and/or intermediatedata, like e.g. color mapping data, which would allow the television todo a further finetuning on the received HDR image) over a cabled networkconnection 410, like e.g. an HDMI interface connection, to a televisionwith 2D LED backlighting (or OLED, etc.) 411. High dynamic rangedisplays can be made in several manners. E.g. one may interleave betweenthe LC material structure which in RGB blocks a lot of light, cellswhich transmit most of the light if driven fully open. Or one may have alaser LED lighted display e.g. in a projector in which one may locallyproject more LEDs to a DMD IC region if suddenly excessive brightness isneeded, or in movie theaters one could have an additional projectorstructure for creating highlights, etc. Or we can wirelessly via anantenna 415 send data to an LDR display 416, like e.g. a tablet display,etc. We also symbolically show another graded image signal beingprovided by the formatter 407, e.g. a medium dynamic range image optimalfor a display of e.g. 1800 nit peak_white, and sent to such a display,or to a storage memory for later use, or via a network to another placeof the user, his mobile phone residing somewhere in the outside world,or one of his friends, etc.

FIG. 5 shows our components know inside a camera 501, which gets a RAWimage from an image sensor 504 through a lens 502. A knowledge engine520 may be configured in various ways to obtain structural, statisticaland/or semantic knowledge by studying captured RAW pictures, and guidethe technical mapping derivation by color mapping derivation unit 214 asexemplified above. The camera may have its own (connected or remote,e.g. from a display apparatus for the director and/or DOP to follow thecapturing, and steer via communication antenna 580) user interface 550for influencing the technical mapping algorithms, like e.g. changing thelocal contrast of some luminance interval. The mapping to GT_IDR may beused to have a quick preview image, where then a HDR image encoding issent, e.g. for final or intermediate recording (in some cases thesteered camera may already do a sufficient LDR-container grading, i.e.encode M_HDR and Im_LDR, but in other cases a first HDR encoding forms abasis for further finetuning grading). This exemplary camera maybroadcast to a receiving side, e.g. via a satellite communicationantenna 599, or alternative communication means.

With FIG. 11 we give a couple of examples of how the various mappingmethods can be realized in 3 (or N) dimensional color space. FIG. 11schematically shows the gamuts of the HDR reference display (for theM_HDR grading) and the LDR reference display (for e.g. GT_IDR, orIm_LDR), in a slice with luminance on the y-axis, and on of thechromatic coordinates namely a saturation S on the x-axis (these may bedefined e.g. as in a CIE definition, and again several options arepossible, e.g. CIE_Lab space etc.). We show how a color defined in M_HDRnamely Col_HDR gets mapped to its LDR-container corresponding colorCol_LDR. The upper part in FIG. 11a is a gamut shape conforming colormapping algorithm as described in (not yet published) EP12187572(PCT/EP2013/069203). The principle is that we first define a functionfor transforming luminances, e.g. along the neutral axis. Then for eachcolor with chromatic coordinates (e.g. hue h and saturation S) we takethe maximum possible luminance Lmax(h, S) for that chromatic color, andscale the luminance mapping function by that value. This guarantees anin-LDR-gamut value for all HDR colors. FIG. 11b shows another possiblecategory of color mappings. Here we just apply any transformation onCol_HDR, so it may end up at Col_LDR1, outside the LDR gamut. Thentypically we follow with a gamut mapping algorithm, which brings thecolor by e.g. desaturating inside the LDR gamut onto Col_LDR2. Insteadof a two-step projection, we can also determine for each luminance whichis the worst situation, i.e. which pixel will be furthest from themaximum saturation for that luminance of the LDR gamut. We can derive adesaturation function DESATPR therefrom, and remap all colors takingthis desaturation into account. There are also other ways to determine adesaturation algorithm. A third category of color mappings will work onRGB space, and then applying mapping functions on those means that thecolors also stay in both gamuts. Any function can be used for handlingcolors, e.g. de local functional remapping of a saturation-correlate,only along those regions of the luma axis where it is desirable, endespecially in a technical grading the actual values matter less, as longas for the to be used gradings reasonable colors can be derivedtherefrom by appropriate further mapping strategies.

We will now elaborate further on useful saturation mappings in an HDRframework, which can be seen separate from other teachings in thisapplication. Brightness and lightness are derived from the human coneresponses, which have an activation state of cone-opsin molecules, andshow how much light is coming from the various colors being a functionof both the object reflectivity characteristics and its illumination(lightness being a greyness estimate compared to a reference white bythe brain analyzing all spatial cone signals from a geometricallyextended complex scene image). Hue is a function of the spectralactivation proportions (per monochromatic or polychromatic activation)of the different cones, and can be estimated from differences in thesecone activations. It serves the determination of dominant colors, e.g.the wide-band nature of various molecules allows identification ofparticular chemical content like e.g. the red ripeness of an apple.Under slowly varying relatively easy to estimate illuminants likesun+skylight, the various discriminatable hues can serve well for manyvisual tasks. Saturation or purity is a measure of how the colorchannels of the ganglion cells and further parts of the visual systemare excited compared to neutral (grey) stimulation. I.e., it is theamount of pure color (e.g. a narrow-band spectrum color) added to aneutral color or vice versa. With the hues topologically ordered on acircle in color space, a radial dimension being a saturation was needed.Painters use the principle by adding a white color to a pure color likered, producing a sequence of tints. In nature saturation is determinedby two important principles. Firstly in specular/glossy media the whiteilluminant is strongly added to the object-colored light from deeperinteractions, leading to strong desaturation, but high saturation innon-specular directions. More importantly, the saturation is related toan amount of pigment, and this may e.g. be used by an animal to judgethe health of a potential mate. Saturation exists in two “variants”.Firstly there is the brightness-dependent one which may be modeled withcolorfulness or chroma, since brighter colors look more saturated. Thiscan be modeled in cone-shaped color spaces in which a color plane (e.g.uv) becomes progressively wider along the brightness axis. The humanbrain can again discount the illumination and judge how intrinsicallysaturated an object is, with monochromatic reflections being thetheoretically most saturated situation. This can be modelled incylindrical spaces, in which the color plane shape stays the same alongthe brightness axis.

Physically such a cone or cylinder could be extended towards infinity,since one can make ever brighter colors, but technologically this makeslittle sense, since any actual recording or reproduction system haslimits. Already the cones of the eye in a certain adaptation state (anamount of cone-opsin made ready in the cone, and intermediate moleculesbeing in a state to multiply any cone-activation sensation, untilultimately a “digital” signal of an amount of pulses along the neuronsin obtained) will at a certain moment bleach so that so many cone-opsinmolecules have been activated that accurate color detection is no longerpossible for some time, which occurs when one looks at a bright lamp. Asimilar thing happens with e.g. a (say slide) photographic recording. Ata certain moment some maximum white must be recorded (and laterreproduced), and scene object luminances above that will clip topeak-white. The same happens for any additive RGB space, whether it isjust an encoding space which may be related with a reference monitor tomake it absolute, or an actual driving signal space for an actualdisplay. Such spaces can be topologically equated with double conespaces. E.g. painters know it as they can make shades with diminishingchroma but the same saturation by adding black to pure colors, and maketints towards pure white at the top of the upper cone. I.e. at the topof such a space there can be only unsaturated (zero-chroma) colors,which is inconvenient in relation to other colors which may exist innature, like e.g. those in a wider gamut of e.g. a higher dynamic rangedisplay. E.g., what to do with a color which has been dimmed downbrightness wise to the LDR (lower dynamic range), but which stillresides in the upper cone? Do we heavily change its saturation, or maybedim down further? And what if that color is just in an intermediatespace which serves to still be boost-mapped to a larger space again?

So for such situations, in addition to theoretical saturations of anycolor, one may need to look at saturation and saturation modificationsin any limited space of allowable colors. Having any mathematicaltransformation within such a space (typically cylindrisized), especiallyuseful if one maps between spaces which can at least be largelycollocated (such as e.g. a scaled [0,1] HDR RGB space on a LDR RGBspace) has the advantage of yielding existing colors, in contrast totransformations which go outside and still need to be translated intorealizable colors, however the non-linear nature of the mathematics maydistort other appearance correlates like lightness or hue. If we maydesign starting and ending gamut/space in 3D in any shape, in principlewe need not worry about that so much since we can design any mappingstrategy.

We can handle a lot of these complications by having a color grader dothe desirable transformations, provided he has a minimal (thoughtypically simple, also taking into account that these transformationstypically indicate mappings needed for sufficiently faithful, or atleast improved compared to blind rendering, determiningrendering-situation dependent corresponding colors for various displays,i.e. the hardware ICs or software in those displays or connected videoprocessing boxes should preferably use only simple mathematicalfunctions, with the complexity being handled by sampling all possible tobe related color rendering scenarios by the grader defining a fewimportant grading situations between which can then be interpolated forother in-between rendering situations) set of mathematicalsaturation-determining functions he can specify.

It is known that mapping between different dynamic ranges can lead tocolors which are either to pastel, or to cartoonishly saturated, and thesituation can be complex with some graders potentially having criticaldesires (e.g one may be critical for faces, but also for the blues inwater, or even the color look of dark clouds).

Our novel saturation processing can be used not only on technicalgradings, but in fact on any graded image (e.g. HDR master, or an LDRgrading; to obtain any other image grading, of a different or similardynamic range; i.e. optimally looking when rendered on e.g. an HDR 2000nit display), and even on raw camera captures, whether introduced intoanother appliance such as a grading computer, or even still in thecamera. To describe the processing in principle we need no input colorspace (which may be the same as the output space, or anything else e.g.a larger space), so we will describe it with the output space of anycolor encoding (whether as intermediate, or device dependent directlyusable for rendering). We will describe the principle with an Luv spaceof the cylindrical type, i.e. the planar directions of which we showonly the u (red-green) axis in FIG. 15a form triangles of the same sizealong the normalized luminance axis L, until the tent starts shrinkingto white. Of course other possibilities can similarly be implemented,and instead of a physical luminance on may use a more psychologicalquantity like e.g. a lightness as the third axis. The gamut of allactually realizable colors is 1501. Now one can apply a mathematicaltransformation which moves colors (either inside or outside the gamut)in a direction of increasing or decreasing saturation, which isrepresented by curve 1503. Although this illustrates the mathematicalprinciple, FIG. 15a may typically also be the user interface view acolor grader sees in one of his subwindows, a main window of courseshowing the color appearance effect of the transformations on a to begraded or re-graded image. In principle we could use any mathematics forthe saturation, but preferably it will be a function which largelydecouples the coordinates, i.e. has mostly an effect on saturation, notor little changing hue or luminance or lightness. In practice (althoughof course the space being only a simplistic model of actual colorappearance, in the end there may be still some visible side effect onnon-saturation aspects of the colors) the mathematics may be anorthogonal one, so although we show a generic variant with a slightlybending saturation change curve (i.e. also slightly lightening colorswhen saturating them), oftentimes this will just be a line in the planeorthogonal to the L axis. To have an easy yet powerful control over thesaturations of objects or regions giving a total look to an image, thegrader has now a possibility to not only define a global saturationmultiplier, but a multiplier which depends on the luminance of colors tobe processed. This function a_s=f(L) may be recorded as a parametricfunction or a lookup table. The relevant luminance Li which defineswhich colors are to be selected for processing is determined by theachromatic color on the curve 1503. Now the only thing needed is somereference saturation level S_ref (1502), which could be equated with anormalized value 1. We assume in this exemplary embodiment that thesaturation is defined as the Euclidean length, i.e. sqrt(u*u+v*v) ande.g. in an Lab space that would be sqrt(a*a+b*b), but of course otherdefinitions would be possible. A practical choice for this referencelevel would be to put it at the (u,v) position of the most saturated ofthe three (R,G,B) or more primaries defining the color space. Now aquick and simple and normally sufficiently precise way to define thesaturation curve a_s=f(L) would be for the grader to determine samplepoints for a number of luminances (level 1504 etc.) on the luminanceaxis. He marks those with dots 1505. The position of those dotsdetermine the saturation, and whether it's a boost or reduction. TheEuclidean distance of dot 1505 to the L axis is compared to the distanceof the reference cylindrical sleeve S_ref, and if it's e.g. 0.3 withS_ref=1, then that means that all colors with that luminance should bedimmed by multiplying their saturation by 0.3 (n.b. multiplicativeoperations should be sufficient for saturation processing, althoughother functions could similarly be used too of course). In the darkerregion dot 1513 specifies a saturation boost for those reds.

So say e.g. that—however the input image was generated, e.g. bydown-mapping from an HDR master—the grader considers that the lightercolors are of sufficient quality, but the darker colors could do with asaturation boost, he may determine a luminance level (e.g. 0.25) and forthat position specify a dot on say 1.8. To save time, the algorithm willdetermine a full curve spanning the entire 0.0-1.0 luminance range fromthat, e.g. a linear interpolation may apply that 1.8 down to the blacks,and apply a 1.0 multiplier to colors above the L−0.25 level (of courseother interpolation strategies may be used by the software like e.g.splines, and the grader can add further dots if he wants to furtherfinetune the color look). Although not necessary, it may be advantageousif the grader also sees the volume of actually occurring colors in theimage 1506. In FIG. 1506 we have only shown the initial situation beforesaturation processing, but typically the final situation (or acontinuously changing volume) will be shown too, which in addition tolooking at the actual processed (intermediate or output) image gives thegrader an idea of where colors move close to the gamut boundary andclipping or soft clipping may occur (there may be a built-in strategy tonon-linearly change the multiplier when within a certain region from thegamut boundary; and such behavior defining options will typically beswitched on or off in software by the grader before starting hisgrading). Although such luminance-only dependencies will for manysituations be sufficient, and may be advantageous if the grader candefine different behaviors for different hues. E.g. he may specify 4LUTs for 4 hue sectors. As explained above, the same saturation boostingor dimming would apply to the red and the green direction from theL-axis, but as seen the volume of colors 1506 may be closer to the gamutboundary in the green direction that in the red direction, e.g. becausethe current shot of the movie, or the current still picture, is of aforrest scene (and in a previous grading saturation may have been sethigh to emulate a sunny look in an LDR encoding). Then the grader maydemarcate sectors of hue, and specify the saturation processing in asimilar manner as described above. A more complex example is also givenwhere a single multiplicative behavior is not sufficient for at leastone luminance level (and maybe hue sector) in the image. E.g. the darkreds may be boosted to make a Ferrari standing in a darker part of theimage (say a garage) look nicer, but when these colors also occur infaces, those faces may become too reddish. Thereto the grader can definea second saturation reference S_ref2 (1510), which will now typicallyalso double as a color region demarcation determining which “face”colors will be processed. Compared to that level, pentagon 1511 nowshows that the saturations there should be dimmed by e.g. 0.75. FIG. 15bthen shows how such behavior will then modify the saturation of colorsof luminances similar to the one of the L level corresponding topentagon 1511. In several situations a discontinuous behavior may besufficient, since the face may occupy a part of color space, and thenthere may be no other colors up to the Ferrari, but smoothingtransitions 1520 may also be applied, either automatically by thesoftware, or finetuned on such a graph in a subwindow by the grader.Also in the other directions, at least luminance and if needed also hue,the grader can determine in which range this behavior should apply, e.g.an upper luminance level 1512 (and similarly a lower luminance levelcould be specified). Outside this range the saturation processing candiscontinuously switch to the other specified behavior, or that may bemore gradually if necessary.

Although such processing could in principle be applied to any situationof saturation processing of any image, it is particularly useful whenchanging between gradings for rendering scenarios with different dynamicrange (i.e. e.g. determine an optimally graded encoding suitable fordriving a 4000 nit HDR display in a dim viewing environment on the basisof an LDR encoding, or vice versa). The HDR space may then be normalizedto the same [0.0, 1.0] range as the LDR space, although that is notnecessary. If this is done in a tunability scenario (in which thegradings are defined to be able to realize a good quality renderingunder various rendering scenarios, typically display peak_white andsurround, where these gradings actually constitute a content-creatorapproved sampling of what the scene should look like under varioussituations, avoiding the complex color appearance modelling problem andconverting it into simple interpolation between representativegradings), the processing will typically be co-encoded as metadata to anencoding of the input image, for any rendering system to suitably applyit (e.g. if a television has a brightness intermediate to the twogradings, e.g. the original being a LDR_100 or 500 nit grading, and thesaturation processing being part of a mapping strategy to obtain a 4000nit grading, a 2000 nit display may decide to do e.g. half the suggestedamount of boosting, or determine a non-linear strategy starting from theco-encoded saturation behavior information).

FIG. 16 a shows an exemplary embodiment of a grading apparatus 1600,arranged to be able to apply a saturation processing to an input imageIm_i (let's say e.g. a LDR grading which needs to converted into amedium dynamic range MDR image for a 1200 nit display; the grader having(at least) such a 1200 nit display 1602 connected to see the result ofhis specifications), and further also encoding of the specification in avideo signal S_o, which typically encodes the video pixels according toa standard like an MPEG standard, and the saturation processing functionas metadata thereto, e.g. in parts of the signal, or separate transportpackets which may be associated with the video by means like a PMT and apresentation time or other means to define a particular image number inthe video with which the processing corresponds (e.g. all images untilthe presentation time of the next saturation processing function data).The grading apparatus comprises at least a saturation processing unit1601, which is arranged to apply the saturation change to an inputimage, according to any of the above elucidated methods. As output itcan give an output image Im_o (e.g. with boosted saturation), but alsoan encoding P_s of the processing function, such as e.g. a LUT a=ai(Li).An encoder 1610 will format this according to the requirements of anagreed (current or future) standardized video signal encoding. It may beadvantageous to facilitate user interaction if there is an imageanalysis unit 1603. This unit will at least look at the definition ofhow the image is encoded, e.g. to determine the R, G, and B triangularpoints of the gamut 1501, but it may also generate e.g. the volume 1506.A user interaction unit 1605 implements (typically in software) allfunctions allowing the user to specify a saturation modificationbehaviour, and in general interact with the image (e.g. define hueboundaries for a particular processing). So it will allow based on userinput usr_inp (e.g. from a keyboard or special grading keyboard) e.g.the dots indicating the amount of saturation boost or dimming to beplaced.

Any receiving apparatus e.g. video processing apparatus 1650 may receivesuch an encoded signal S_o, and apply the specified saturationprocessing either directly, or derive its own optimal saturationprocessing on the basis thereof. The video processing apparatuscomprises at least a saturation processing unit 1651, arranged to applysuch a luminance-dependent saturation strategy as described above on theinput image Im_i. This input image may be obtained in various ways, buttypically the video processing apparatus 1650 may comprise a decoder1653, arranged to do e.g. AVC or HEVC video decoding to obtain apixellized color image Im_i, and the metadata decoding of the saturationprocessing functions, coverting it to an internally usable format (e.g.this information could be encoded in various manners, like run lengthencoding, or the decoder may want to convert the specification intoanother one of different precision etc.). In general the saturationprocessing will form part of a general color processing/mappingperformed by a color processing unit 1652, which may also map theluminances of the colors of Im_1 to new values (e.g. if the input imageis an HDR image encoded on [0.0-1.0], the darker parts may be too darkto be used for LDR rendering and may need to be brightened, eitherbefore (preferably) or after saturation processing). The videoprocessing apparatus 1650 outputs an output image Im_o, which may e.g.be directly be suitable on a particular display (there may of course befurther conversion such as to take display aspects like its internalEOTF into account, but that is unimportant for the present discussion),or the Im_o may be outputted for other use, e.g. storing on a memorylike a blu-ray disk, or on a video server etc. Such a video processingapparatus 1650 may e.g. be incorporated in a television, computer orsettopbox, or a professional apparatus like e.g. a digital cinema videohandler for in the cinema, or a computer system of an image analysisdepartment etc.

For further elucidation we give two examples of possible use in atunability scenario in FIG. 17. In FIG. 17a we want to derive LDR colorsfrom a HDR master grading, according to criteria of the grader like goodlocal contrast, simulated appearance of light sources or bright areasetc. For the tone mapping in the luminance direction we assume we use achromaticity (u,v) preserving mapping, but we don't want to scaleeverything to the maximum of the output gamut like in EP12187572. Thisrisks for some colors to fall outside of the output gamut G_LDR, even ifwe bring all luminances to within the range topped by L_LDRm. The gradercan solve this technical problem by an artistic optimal balance ofbrightness versus saturation, by prior to luminance down-mapping doing asaturation decrease in the HDR input color space (arrow 1701). FIG. 17bgives another example, this time with an intermediate encoding. What wesee is the input and output space (and gamut) being defined in asimilarly normalized hence collocated way. We have an intermediate imageencoding of an HDR image (i.e. an image of sufficient luminanceinformation to be usable for HDR rendering), which however has beenencoded (tuned) somewhat to be also still reasonably renderable on alower dynamic range display (either directly or with some finaloptimizing color mapping typically implement by a display-side colormapping unit, e.g. inside the display). This means that e.g. a brightoutdoors region has been encoded with luminance values there were theluminance mapping arrow TM_L2H starts. The tradeoff was to give somebrightness kick in these image regions and their colors (when used e.g.directly in a lower dynamic range rendering), then the saturation neededto be reduced due to the mathematical shape of the gamut. For HDRrendering one wants these regions bright, but not near the maximum ofthe gamut, since those luminances are reserved for lamps and explosions,i.e. in the normalized HDR output gamut G_HDR, one needs to transformthe colors giving them lower luminances (in luminance regions L_os). Nowthese colors look paler than they should (could) be, so the grader willco-specify a saturation boost for obtaining the final rendering, but forthose luminances (at least), because other regions of color space may befine.

Typically the above will be realized as various embodiments of an imagecolor grading apparatus (1600) comprising:

-   -   an input (240) for a color input image (Im_i), and    -   user interaction unit (1605) arranged to allow a color grader to        specify a saturation processing strategy comprising at least        first saturation change factor for a first range of luminances        of colors to be processed, and a different second saturation        change factor for a second range of luminances of the colors to        be processed, the first and second saturation change factors        preferably being multiplicative.

The characterizing at least one factor for the luminance-dependentsaturation change could be various, e.g. a coefficient could specifyparabolic or sigmoidal saturation change behavior along at least oneconstant (or approximately constant) luminance line (e.g. the sigmoidalbehavior in a plot like in FIG. 15 b may start with a small saturationdimming, and then sigmoidal grow to a large boosting for higher valuesuntil some maximum which would start clipping a considerable amount ofhighly saturated colors in the input image, but of course a furtherparameter could be encoded for that luminance level, for decreasing thesaturation boost again in those areas to 1.0 or even below, to makethose colors fit better in the available gamut), but in many situationsa multiplicative factor changing an input saturation s_in into outputsaturation s_out=a*s_in will be of sufficient control complexity andvisual precision.

Although some embodiments may only specify for one or a small region ofluminances a saturation processing characterizing factor (the rest ofthe colors e.g. defaulting to staying the same which would be identicalto multiplying with a factor 1.0), it may be advantageous to specifyfactors for the entire possible luminance range of colors in the inputimage (e.g. 0.0-1.0) or some other luminance range, of which some colorsmay have saturation processing defined even if they do not occur in theinput image. This can be done either by actually specifying them (e.g.the algorithm creating a continuous interpolation and the grader eitheraccepting or correcting that), which may be co-encoded in the imagesignal S_o as e.g. a LUT of sufficient precision (which could still befurther interpolated at a receiver side), but it is sufficient if therequired processing for each possible color with luminance Li isderivable, i.e. the metadata specifying the saturation processingstrategy for a receiver may just comprise functional parameters, orpositions of dots like 1505 etc.

If more precision is required, it may be advantageous if that userinteraction unit (1605) allows specification of saturation processingbased on further properties of colors in color space, such as e.g. a huedependency, like e.g. s_out=fi(L, h_i), in which there is a set of h_i'sbeing centroid hues for hue sectors, and a color (u,v) is processed by asaturation mapping based on nearest proximity to all those centroidhues, or another hue-dependent definition s_out=fi(L, f_hi( )) in whichf_hi( ) is some function or algorithmic strategy mapping the hue of aninput color to some coefficient i, which defines a particular saturationprocessing strategy. Similarly, there may be various strategies fordifferent saturation subranges of at least one luminance range (at oraround Li), and one could treat the complementary hues as if they wereof negative saturation. This may be mathematically defined as e.g.s_out=fi(L, f si( )) in which now there is a categorical (e.g. booleanif two regions are involved) allocation based on the saturation ofcolors (u,v) of the input image to be processed. Although this precisionwill usually be sufficient, one may in general define strategies whichdiffer based on both hue and saturation of colors in selected luminancesubranges.

So we described a method of specifying a saturation processing strategyfor an input image (Im_i), comprising specifying at least firstsaturation change factor for a first range of luminances of colors ofthe input image to be processed, and a different second saturationchange factor for a second range of luminances of other colors of theinput image to be processed, and preferably comprising an encoding ofthis strategy as metadata associated with the input image, and variantsthereof.

A complementary apparatus thereto will be a video processing apparatus(1650) comprising;

-   -   an input for an input image (Im_i), and    -   a saturation processing unit (1651) arranged to apply a first        saturation change to colors of the input image falling in a        first range of luminances, and a different second saturation        change to colors of the input image falling in a second range of        luminances, the video processing apparatus comprising means to        obtain a first and a second saturation change factor        characterizing the first respectively second saturation change,        wherein this means preferably comprises a decoder to decode the        first and a second saturation change factor from metadata in an        image signal (S_o). Although this apparatus may be part of a        system at a single location or in single use, typically a grader        or re-grader for existing content will specify the gradings        once, and then at a later occasion and different the usage of        these gradings will happen by the video processing apparatus.        This may e.g. typically be a consumer apparatus. The consumer        may have bought a movie over the internet, which he has watched        e.g. 5 years ago on his LDR display. Now, still having the        rights to view the content, he indicates to the management        module on the server that he has bought a HDR display, and he        desires to receive the metadata for the images of the video        program, specifying inter alia this saturation. Of course the        user may also purchase the video encoding (pixellized image        colors)+the color (saturation) processing metadata on a single        memory product, such as e.g. a bluray disk, solid state memory        stick, or pre-installed on e.g. a video player device like a        portable player etc.

This corresponds with a method of video processing comprising applying afirst saturation change to colors of an input image falling in a firstrange of luminances, and a different second saturation change to colorsof the input image falling in a second range of luminances, and thevarious embodiments thereof according to the above explained elucidatingprinciples.

FIG. 13 shows an example of how different LDR images can be obtained forrendering. In this example we have chosen a smooth curve 1301 fortechnical grading, which allows to recover all luminance ranges of theoriginal to be encoded master HDR image (whatever range that may havehad) to a reasonable precision. When we save this in the technicallygraded LDR image GT_IDR, a “dumb” legacy system will although thepicture will be recognizable, render a somewhat bland picture on an LDRdisplay, with unpreferred contrast in the main regions like the actor.Any such system could use automatic processing to increase that contrastor otherwise try to optimize the picture, but would need to do thatblindly. It would be much better if the content provider can encode whatthe receiving side can do to make a better LDR grading than thetechnical grading. The data required for specifying such second tonemapping from the technical grade GT_IDR, can be as simple as specifyingtwo boundaries gt_Mh and gt_Ml which indicate where the main informationresides in code space, and which other colors may be (seriously)deteriorated at the costs of others. The receiving side system need thenonly stretch the lumas taking these important values into account. Wehave shown this in a graph 1302, which when applied directly to thedisplay (with known calibrated properties, e.g. standard gamma andviewing environment behavior) will result in rendered luminances on thex—as according to that graph. In this example the receiving end colormapper has decided to majorly stretch the blacks, retaining a little ofposterized information of the HDR still, however that may be renderedunder the giving surround environment, and has decided to use a hardclipping strategy, mapping gt_Mh to white (i.e. defining it as the LDRwhite in the total HDR color space). All colors above can then not berendered on this e.g 700 nit display. Of course more complexspecifications may be co-encoded about what a receiving side colormapping should do with the received GT_IDR encoded image, to obtainoptimal looks on one or more intended displays (e.g. 700 nit doprocessing X, 1500 nit do Y), and this may all be defined in colormapping functions and applied on the basis of the received GT_IDR image(e.g. further characteristic gray values can help in furtherparametrically specified improvement of the to be obtained LDR grading,or 1 explicit mapping strategy may be specified for the entire range,per receiving display category, and this can be done e.g. with a LUT ofboost factors [between 1/X and Y] per luminance value). So simpledecoding systems will render a reasonable LDR picture, and decoderscapable of handling all the present possibilities will yield an optimalLDR or HDR or any MDR (medium dynamic range), or ODR (outside typicaldynamic range, like subLDR with extremely low contrast) images. Althoughour framework allows for specifying exact gradings for N LDR (and other)viewing scenarios (e.g. 100 nit and 500 nit tv under dark, and dim, andbright viewing scenarios=6 gradings), it is of course not alwaysnecessary to render an optimal grading, but rather a good quality imagewill also do in some scenarios. We illustrate this with FIG. 13 as amere example. Let's suppose we have a news cast with HDR lighting whichis supposed to look very nice, but the LDR being an approximation shouldjust look good, and the grader should be able to define his system in acouple of seconds prior to starting the studio news show. Thereto onemay define two additional demarcation thresholds gt_H2 and gt_L2, sothat the receiving end can decide how to color map the GT_IDR to obtainits display driving image. E.g. it may be defined (by con-encoding thesevalues in specifically reserved codes like MINIMAL_LDR_Low andMINIMAL_LDR_High, or BROAD_LDR_Low and BROAD_LDR_High, or even more LDRsubrange delimiters) that gt_ML and gt_Mh are the “ultimate” delimitersof the main action LDR subrange of the HDR scene, which still containsome of the HDR information (like already some brighter parts in thestudio), and gt_L2 and gt_L2 contain the “absolute minimum” required forLDR rendering (e.g. no (severe) clipping of highlights in the faces).The receiving end color mapping can then select its strategy for makinga LDR image. E.g. it may define a proprietary soft clipping strategy tothe regions between gt_ML and gt_L2 and gt_H2 and gt_H2, after havingdefined a stretching strategy for the middle range of absolutelynecessarily well-rendered colors between gt_L2 and gt_H2 (e.g. mappingthese to values 20 and 220). But if the receiving system decides to do ahard stretch mapping the [gt_L2, gt_H2] range to [0,255] and clippingoutside, the LDR rendering will also look reasonable. The receiving endcould decide to choose an option e.g. on the basis of the amount ofavailable surround lighting. So we see the system leaves a lot ofpossibilities, from tightly controlled complex grading definitionsystems, to really simple systems having only a few guiding parametersco-encoded. The dynamic range look of a technical grading can e.g. beLDR, or MDR (i.e. looking good on a reference display of e.g. 1200 nit).But the principle is always decoupling the technical requirements (suchas reversibility, which is handled in the HDR-GT_IDR relationship) fromthe artistic freedom (making an arbitrary recoloring of all LDR imageobjects as far as desired by the grader from GT-IDR, and as complex amapping function as needed, though typically with a number of supportedbasis functions (which the decoder needs to support), like e.g.multi-subfunction luma and color mapping (e.g. with LUTs), definition oflocal object segments and mapping functions therefore, etc.). The userinterfaces can be very simple for the grader, e.g. as for many systemsthe precise finetuned position of gt_Mh, gt_H2 etc. may not be critical,he may define them by quickly scribbling onto a couple of regions of thecurrently captured image of a scene, like e.g. the newsreader's face,the desk behind which she is sitting, and if required with another pen(defining the outer regions, like brights above gt_Mh) the luminousscreen behind her back. Of course more information can be inputted—e.g.with more gt characteristic points—like e.g. shadows or highlights inthe HDR lighting of her face, or the table, and this may all be used formore complex color mapping strategies. And further specifications ofthese regions may be done, e.g. a geometrical function across her facedefining a contrast trajectory, and functions to redefine/remap thoseunder various conditions (e.g. leave the brights end of the curve, butbrighten the darks a little), etc. All this can be added to the metadataif required, but in general one will prefer simple systems with theminimal amount of required data, and at least one LDR range may beuseful (but a second one around a person's colors may be useful too insome scenarios).

FIG. 14 shows an example of how technical gradings can also work oncolorimetric principles. Suppose we have a mathematical color space 1401with primaries definition so that less saturated colors can be made thanone may need for some (maybe future) envisaged displays, with physicalgamut 1402. That may be not such an issue for the darker colors, sincethe display may do some boosting of the saturation, and there may beenough recorded information for that to work well (perhaps by applying apost-banding removal filter if needed). However in the tent of the gamutthere could be a problem, and this is where we may like to have somesaturated high brightness colors instead of more pastellized ones. Ifthis is an issue, the grader can decide to define his grading up to anew white point W* (by topping off the tent, leaving a possibility ofdefining more saturated colors near the maximally bright colors), butthen to avoid confusion, this white point W* (being the brightestpossible color according to this code definition) may be co-encoded (tostate it's not just a scene with “no white”). Of course the receivingend may also just consider what the brightest encoding is in the inputimage, and do a rendering therewith, since the visual system adapts witha grey looking as a white for bright displays anyway, but then an LDRsystem may use it to boost some parts of the picture to its maximumbrightness.

The skilled person will understand that many variants are possible forthe above concepts. E.g., although in the particular elucidatingexamples in the Figures we assumed that the data of the color mappingswas co-encoded with the image pixel data (GT_IDR), e.g. as metadatawithin placeholders defined in the image coding standard like e.g. SEImessages or similar, or within a reserved section of the memory e.g. asection of the BD, of course other examples can transmit the colormapping data via another communication channel than the GT_IDR. E.g. thecontent creator can put additional constraints on the properties of thecolor mappings or resulting GT_IDR, e.g. he may give it a totallydifferent look than M_HDR and Im-LDR, or even an ugly picture, andsupply the color mapping data via a secure channel upon verification ofthe receiver, or payment, etc.

The algorithmic components disclosed in this text may (entirely or inpart) be realized in practice as hardware (e.g. parts of an applicationspecific IC) or as software running on a special digital signalprocessor, or a generic processor, etc. They may be semi-automatic in asense that at least some user input may be/have been (e.g. in factory,or consumer input, or other human input) present.

It should be understandable to the skilled person from our presentationwhich components may be optional improvements and can be realized incombination with other components, and how (optional) steps of methodscorrespond to respective means of apparatuses, and vice versa. The factthat some components are disclosed in the invention in a certainrelationship (e.g. in a single figure in a certain configuration)doesn't mean that other configurations are not possible as embodimentsunder the same inventive thinking as disclosed for patenting herein.Also, the fact that for pragmatic reasons only a limited spectrum ofexamples has been described, doesn't mean that other variants cannotfall under the scope of the claims. In fact, the components of theinvention can be embodied in different variants along any use chain,e.g. all variants of a creation side like an encoder may be similar asor correspond to corresponding apparatuses at a consumption side of adecomposed system, e.g. a decoder and vice versa. Several components ofthe embodiments may be encoded as specific signal data in a signal fortransmission, or further use such as coordination, in any transmissiontechnology between encoder and decoder, etc. The word “apparatus” inthis application is used in its broadest sense, namely a group of meansallowing the realization of a particular objective, and can hence e.g.be (a small part of) an IC, or a dedicated appliance (such as anappliance with a display), or part of a networked system, etc.“Arrangement” or “system” is also intended to be used in the broadestsense, so it may comprise inter alia a single physical, purchasableapparatus, a part of an apparatus, a collection of (parts of)cooperating apparatuses, etc.

The computer program product denotation should be understood toencompass any physical realization of a collection of commands enablinga generic or special purpose processor, after a series of loading steps(which may include intermediate conversion steps, such as translation toan intermediate language, and a final processor language) to enter thecommands into the processor, to execute any of the characteristicfunctions of an invention. In particular, the computer program productmay be realized as data on a carrier such as e.g. a disk or tape, datapresent in a memory, data traveling via a network connection—wired orwireless—, or program code on paper. Apart from program code,characteristic data required for the program may also be embodied as acomputer program product. Such data may be (partially) supplied in anyway.

The invention or any data usable according to any philosophy of thepresent embodiments like video data, may also be embodied as signals ondata carriers, which may be removable memories like optical disks, flashmemories, removable hard disks, portable devices writeable via wirelessmeans, etc.

Some of the steps required for the operation of any presented method maybe already present in the functionality of the processor or anyapparatus embodiments of the invention instead of described in thecomputer program product or any unit, apparatus or method describedherein (with specifics of the invention embodiments), such as data inputand output steps, well-known typically incorporated processing stepssuch as standard display driving, etc. We also desire protection forresultant products and similar resultants, like e.g. the specific novelsignals involved at any step of the methods or in any subpart of theapparatuses, as well as any new uses of such signals, or any relatedmethods.

By image signal we typically mean any of the existing or similar ways topack image data. Apart from a pixellized structure of color tuplets,which we call an image (or picture), such a signal may contain metadatalike descriptors for the meaning of the data like e.g. the image aspectratio, and further metadata containing useful information relating tothe encoded image, such as for modifying it at a receiving side, etc.Signals may have various physical/technical forms of embodiments, e.g.the may be defined as electrical modulations of a carrier wave, or bitsrepresented as mechanical pits, or material modifications like e.g. alocal magnetization state, etc.

It should be noted that the above-mentioned embodiments illustraterather than limit the invention. Where the skilled person can easilyrealize a mapping of the presented examples to other regions of theclaims, we have for conciseness not mentioned all these optionsin-depth. Apart from combinations of elements of the invention ascombined in the claims, other combinations of the elements are possible.Any combination of elements can be realized in a single dedicatedelement.

Any reference sign between parentheses in the claim is not intended forlimiting the claim, nor is any particular symbol in the drawings. Theword “comprising” does not exclude the presence of elements or aspectsnot listed in a claim. The word “a” or “an” preceding an element doesnot exclude the presence of a plurality of such elements.

The invention claimed is:
 1. An image encoder comprising: an input for ahigh dynamic range input image; an image grading unit arranged to allowa human color grader to specify a color mapping from a representation ofthe high dynamic range input image, to a first low dynamic range imageby a human-determined color mapping algorithm, and configured to outputdata specifying the color mapping; and an automatic grading unitconfigured to derive a second low dynamic range image by applying anautomatic color mapping algorithm to the high dynamic range input image,with a color mapping algorithm fulfilling a condition that a HDRreconstructed image falling within a second predefined accuracy from thehigh dynamic range input image can be calculated by applying a secondcolor mapping algorithm which is the inverse of the automatic colormapping algorithm to the second low dynamic range image.
 2. The imageencoder as claimed in claim 1, wherein the representation of the highdynamic range input image is one of the high dynamic range input imageand the second low dynamic range image.
 3. The image encoder as claimedin claim 1, wherein the image grading unit and the automatic gradingunit are configured to apply a monotonous mapping function on aluminance-correlate of pixels in their respective input image, in atleast a geometrical region of the respective input image correspondingto a same geometrical region of the high dynamic range input image. 4.The image encoder as claimed in claim 3, wherein the automatic gradingunit is configured to determine the monotonous mapping function fromluminance-correlates of pixels of the high dynamic range input image toluminance-correlates of pixels of the second low dynamic range imageaccording to a criterion which determines respective ranges ofluminance-correlates of pixels of the high dynamic range input imageallocated to respective single values of a luminance-correlate of pixelsof the second low dynamic range image, the respective ranges forming aset of luminance-correlate ranges covering the total range of possibleluminance-correlate values for the high dynamic range input image. 5.The image encoder as claimed in claim 1, comprising a data formatterconfigured to output into an image signal the second low dynamic rangeimage and at least one of, or both of, data describing the color mappingbetween the high dynamic range input image and the second low dynamicrange image, and data describing the color mapping between the first lowdynamic range image and the second low dynamic range image.
 6. An imagedecoder configured to receive via an image signal input an image signalcomprising a low dynamic range image, and data describing a first colormapping enabling reconstruction of a reconstruction of a high dynamicrange image on the basis of the low dynamic range image, and datadescribing a second color mapping allowing calculation of a further lowdynamic range image on the basis of the low dynamic range image, theimage decoder comprising an image derivation unit configured to deriveat least the low dynamic range image) on the basis of the datadescribing the second color mapping and the pixel colors encoded in thelow dynamic range image.
 7. The image decoder as claimed in claim 6,comprising a system configuration unit, configured to determine whetherthe decoder is connected to at least one of a high dynamic range displayand a low dynamic range display, and the system configuration unit beingarranged to configure the image derivation unit to determine at leastthe reconstruction in case of a connection to the high dynamic rangedisplay, and arranged to configure the image derivation unit todetermine at least the further low dynamic range image in case of aconnection to the low dynamic range display.
 8. The image decoder asclaimed in claim 6, further comprising: an output port including atleast one of a wired connection and a wireless connection to anyconnectable display; and a signal formatter configured to transmit atleast one of the reconstruction of the high dynamic range image and thelow dynamic range image to any connected display.
 9. The image decoderas claimed in claim 6, wherein the image derivation unit is configuredto determine a further image based on the reconstruction of the highdynamic range image and the low dynamic range image, or the second lowdynamic range image and data describing the first color mapping and datadescribing the second color mapping.
 10. The image decoder as claimed inclaim 6, wherein the image signal input is connected to a reading unitconfigured to read the image signal from a memory.
 11. The image decoderas claimed in claim 6, wherein the image signal input is connectable viaa network connection to a source of the image signal.